DataWraith's ArXiv Frontpage

Last updated: 2025-04-13

This frontpage is made by scraping ArXiv's computer science RSS feed and tagging papers with a classifier.

Each tag is weighted according to my preferences to compute a paper's interestingness score.

48.5921 Controller Distillation Reduces Fragile Brain-Body Co-Adaptation and Enables Migrations in MAP-Elites
Authors: Alican Mertan, Nick Cheney | Date: 2025-04-11
Brain-body co-optimization suffers from fragile co-adaptation where brains become over-specialized for particular bodies, hindering their ability to transfer well to others. Evolutionary algorithms tend to discard such low-performing solutions, eliminating promising morphologies. Previous work consi ... more
Quality Diversity
48.4225 Tabular and Deep Reinforcement Learning for Gittins Index
Authors: Harshit Dhankhar, Kshitij Mishra, Tejas Bodas | Date: 2025-04-11
In the realm of multi-arm bandit problems, the Gittins index policy is known to be optimal in maximizing the expected total discounted reward obtained from pulling the Markovian arms. In most realistic scenarios however, the Markovian state transition probabilities are unknown and therefore the Gitt ... more
Multi-armed Bandit
44.2313 Inducing Programmatic Skills for Agentic Tasks
Authors: Zora Zhiruo Wang, Apurva Gandhi, Graham Neubig, Daniel Fried | Date: 2025-04-11
To succeed in common digital tasks such as web navigation, agents must carry out a variety of specialized tasks such as searching for products or planning a travel route. To tackle these tasks, agents can bootstrap themselves by learning task-specific skills online through interaction with the web e ... more
Cool
40.2623 Dynamic Content Caching with Waiting Costs via Restless Multi-Armed Bandits
Authors: Ankita Koley, Chandramani Singh | Date: 2025-04-11
We consider a system with a local cache connected to a backend server and an end user population. A set of contents are stored at the the server where they continuously get updated. The local cache keeps copies, potentially stale, of a subset of the contents. The users make content requests to the l ... more
40.1387 OLMoTrace: Tracing Language Model Outputs Back to Trillions of Training Tokens
Authors: Jiacheng Liu, Taylor Blanton, Yanai Elazar, Sewon Min, YenSung Chen, Arnavi Chheda-Kothary, Huy Tran, Byron Bischoff, Eric Marsh, Michael Schmitz, Cassidy Trier, Aaron Sarnat, Jenna James, Jon Borchardt, Bailey Kuehl, Evie Cheng, Karen Farley, Sruthi Sreeram, Taira Anderson, David Albright, Carissa Schoenick, Luca Soldaini, Dirk Groeneveld, Rock Yuren Pang, Pang Wei Koh, Noah A. Smith, Sophie Lebrecht, Yejin Choi, Hannaneh Hajishirzi, Ali Farhadi, Jesse Dodge | Date: 2025-04-11
We present OLMoTrace, the first system that traces the outputs of language models back to their full, multi-trillion-token training data in real time. OLMoTrace finds and shows verbatim matches between segments of language model output and documents in the training text corpora. Powered by an extend ... more
Cool
38.7243 AssistanceZero: Scalably Solving Assistance Games
Authors: Cassidy Laidlaw, Eli Bronstein, Timothy Guo, Dylan Feng, Lukas Berglund, Justin Svegliato, Stuart Russell, Anca Dragan | Date: 2025-04-11
Assistance games are a promising alternative to reinforcement learning from human feedback (RLHF) for training AI assistants. Assistance games resolve key drawbacks of RLHF, such as incentives for deceptive behavior, by explicitly modeling the interaction between assistant and user as a two-player g ... more
Expert Iteration
34.4566 Hyperparameter Optimisation with Practical Interpretability and Explanation Methods in Probabilistic Curriculum Learning
Authors: Llewyn Salt, Marcus Gallagher | Date: 2025-04-11
Hyperparameter optimisation (HPO) is crucial for achieving strong performance in reinforcement learning (RL), as RL algorithms are inherently sensitive to hyperparameter settings. Probabilistic Curriculum Learning (PCL) is a curriculum learning strategy designed to improve RL performance by structur ... more
HPO and AutoML
31.7438 Hyperparameter Optimization in Machine Learning
Authors: Luca Franceschi, Michele Donini, Valerio Perrone, Aaron Klein, C\'edric Archambeau, Matthias Seeger, Massimiliano Pontil, Paolo Frasconi | Date: 2025-04-11
Hyperparameters are configuration variables controlling the behavior of machine learning algorithms. They are ubiquitous in machine learning and artificial intelligence and the choice of their values determines the effectiveness of systems based on these technologies. Manual hyperparameter search is ... more
HPO and AutoML
19.6005 Covariant Gradient Descent
Authors: Dmitry Guskov, Vitaly Vanchurin | Date: 2025-04-11
We present a manifestly covariant formulation of the gradient descent method, ensuring consistency across arbitrary coordinate systems and general curved trainable spaces. The optimization dynamics is defined using a covariant force vector and a covariant metric tensor, both computed from the first ... more
Optimizers
10.4338 Algorithm Discovery With LLMs: Evolutionary Search Meets Reinforcement Learning
Authors: Anja Surina, Amin Mansouri, Lars Quaedvlieg, Amal Seddas, Maryna Viazovska, Emmanuel Abbe, Caglar Gulcehre | Date: 2025-04-11
Discovering efficient algorithms for solving complex problems has been an outstanding challenge in mathematics and computer science, requiring substantial human expertise over the years. Recent advancements in evolutionary search with large language models (LLMs) have shown promise in accelerating t ... more
Evolutionary Algorithms
10.2761 Genetic Programming for Explainable Manifold Learning
Authors: Ben Cravens, Andrew Lensen, Paula Maddigan, Bing Xue | Date: 2025-04-11
Manifold learning techniques play a pivotal role in machine learning by revealing lower-dimensional embeddings within high-dimensional data, thus enhancing both the efficiency and interpretability of data analysis by transforming the data into a lower-dimensional representation. However, a notable c ... more
Evolutionary Algorithms
5.6113 RO-FIGS: Efficient and Expressive Tree-Based Ensembles for Tabular Data
Authors: Ur\v{s}ka Matja\v{s}ec, Nikola Simidjievski, Mateja Jamnik | Date: 2025-04-11
Tree-based models are often robust to uninformative features and can accurately capture non-smooth, complex decision boundaries. Consequently, they often outperform neural network-based models on tabular datasets at a significantly lower computational cost. Nevertheless, the capability of traditiona ... more
Decision Trees
3.3563 Cellular Network Design for UAV Corridors via Data-driven High-dimensional Bayesian Optimization
Authors: Mohamed Benzaghta, Giovanni Geraci, David L\'opez-P\'erez, Alvaro Valcarce | Date: 2025-04-11
We address the challenge of designing cellular networks for uncrewed aerial vehicles (UAVs) corridors through a novel data-driven approach. We assess multiple state-of-the-art high-dimensional Bayesian optimization (HD-BO) techniques to jointly optimize the cell antenna tilts and half-power beamwidt ... more
Bayesian Optimization
3.2248 Optimizing Through Change: Bounds and Recommendations for Time-Varying Bayesian Optimization Algorithms
Authors: Anthony Bardou, Patrick Thiran | Date: 2025-04-11
Time-Varying Bayesian Optimization (TVBO) is the go-to framework for optimizing a time-varying, expensive, noisy black-box function. However, most of the solutions proposed so far either rely on unrealistic assumptions on the nature of the objective function or do not offer any theoretical guarantee ... more
Bayesian Optimization
3.1027 Robust Reinforcement Learning from Human Feedback for Large Language Models Fine-Tuning
Authors: Kai Ye, Hongyi Zhou, Jin Zhu, Francesco Quinzan, Chengchung Shi | Date: 2025-04-11
Reinforcement learning from human feedback (RLHF) has emerged as a key technique for aligning the output of large language models (LLMs) with human preferences. To learn the reward function, most existing RLHF algorithms use the Bradley-Terry model, which relies on assumptions about human preference ... more
Reinforcement Learning
3.0745 SPoRt -- Safe Policy Ratio: Certified Training and Deployment of Task Policies in Model-Free RL
Authors: Jacques Cloete, Nikolaus Vertovec, Alessandro Abate | Date: 2025-04-11
To apply reinforcement learning to safety-critical applications, we ought to provide safety guarantees during both policy training and deployment. In this work we present novel theoretical results that provide a bound on the probability of violating a safety property for a new task-specific policy i ... more
Reinforcement Learning
2.7853 F5R-TTS: Improving Flow-Matching based Text-to-Speech with Group Relative Policy Optimization
Authors: Xiaohui Sun, Ruitong Xiao, Jianye Mo, Bowen Wu, Qun Yu, Baoxun Wang | Date: 2025-04-11
We present F5R-TTS, a novel text-to-speech (TTS) system that integrates Gradient Reward Policy Optimization (GRPO) into a flow-matching based architecture. By reformulating the deterministic outputs of flow-matching TTS into probabilistic Gaussian distributions, our approach enables seamless integra ... more
Reinforcement Learning
2.2695 Multihead self-attention in cortico-thalamic circuits
Authors: Arno Granier, Walter Senn | Date: 2025-04-11
Both biological cortico-thalamic networks and artificial transformer networks use canonical computations to perform a wide range of cognitive tasks. In this work, we propose that the structure of cortico-thalamic circuits is well suited to realize a computation analogous to multihead self-attention, ... more
Attention
2.2456 Multi-Fidelity Policy Gradient Algorithms
Authors: Xinjie Liu, Cyrus Neary, Kushagra Gupta, Christian Ellis, Ufuk Topcu, David Fridovich-Keil | Date: 2025-04-11
Many reinforcement learning (RL) algorithms require large amounts of data, prohibiting their use in applications where frequent interactions with operational systems are infeasible, or high-fidelity simulations are expensive or unavailable. Meanwhile, low-fidelity simulators--such as reduced-order m ... more
Reinforcement Learning
2.1903 Single-Source Shortest Path Problem in Weighted Disk Graphs
Authors: Shinwoo An, Eunjin Oh, Jie Xue | Date: 2025-04-11
In this paper, we present efficient algorithms for the single-source shortest path problem in weighted disk graphs. A disk graph is the intersection graph of a family of disks in the plane. Here, the weight of an edge is defined as the Euclidean distance between the centers of the disks correspondin ... more
Pathfinding
1.693 CAT: Circular-Convolutional Attention for Sub-Quadratic Transformers
Authors: Yoshihiro Yamada | Date: 2025-04-11
Transformers have driven remarkable breakthroughs in natural language processing and computer vision, yet their standard attention mechanism still imposes O(N^2) complexity, hindering scalability to longer sequences. We introduce Circular-convolutional ATtention (CAT), a Fourier-based approach that ... more
Attention
1.2896 Learning-Inspired Fuzzy Logic Algorithms for Enhanced Control of Oscillatory Systems
Authors: Vuong Anh Trung, Thanh Son Pham, Truc Thanh Tran, Tran le Thang Dong, Tran Thuan Hoang | Date: 2025-04-11
The transportation of sensitive equipment often suffers from vibrations caused by terrain, weather, and motion speed, leading to inefficiencies and potential damage. To address this challenge, this paper explores an intelligent control framework leveraging fuzzy logic, a foundational AI technique, t ... more
Fuzzy Logic
0.9575 Faster Algorithms for Reverse Shortest Path in Unit-Disk Graphs and Related Geometric Optimization Problems: Improving the Shrink-and-Bifurcate Technique
Authors: Timothy M. Chan, Zhengcheng Huang | Date: 2025-04-11
In a series of papers, Avraham, Filtser, Kaplan, Katz, and Sharir (SoCG'14), Kaplan, Katz, Saban, and Sharir (ESA'23), and Katz, Saban, and Sharir (ESA'24) studied a class of geometric optimization problems -- including reverse shortest path in unweighted and weighted unit-disk graphs, discrete Fr\' ... more
Pathfinding
-0.1835 PyIT2FLS: A New Python Toolkit for Interval Type 2 Fuzzy Logic Systems
Authors: Amir Arslan Haghrah, Sehraneh Ghaemi | Date: 2025-04-11
Fuzzy logic is an accepted and well-developed approach for constructing verbal models. Fuzzy based methods are getting more popular, while the engineers deal with more daily life tasks. This paper presents a new Python toolkit for Interval Type 2 Fuzzy Logic Systems (IT2FLS). Developing software too ... more
-1.2265 The Cloud and Flock Polynomials of q-Matroids
Authors: Heide Gluesing-Luerssen, Benjamin Jany | Date: 2025-04-11
We show that the Whitney function of a q-matroid can be determined from the cloud and flock polynomials associated to the cyclic flats. These polynomials capture information about the corank (resp., nullity) of certain spaces whose cyclic core (resp., closure) is the given cyclic flat. Going one ste ... more
Math
-1.3207 Solving "pseudo-injective" polynomial equations over finite dynamical systems
Authors: Antonio E. Porreca, Marius Rolland | Date: 2025-04-11
We consider the semiring of abstract finite dynamical systems up to isomorphism, with the operations of alternative and synchronous execution. We continue searching for efficient algorithms for solving polynomial equations of the form $P(X) = B$, with a constant side B, with the goal of decomposing ... more
Math
-1.4323 A 2-6 GHz Ultra-Wideband CMOS Transceiver for Radar Applications
Authors: Alin Thomas Tharakan, Prince Philip, Gokulan T., Sumit Kumar, Gaurab Banerjee | Date: 2025-04-11
This paper presents a low power, low cost transceiver architecture to implement radar-on-a-chip. The transceiver comprises of a full ultra-wideband (UWB) transmitter and a full UWB band receiver. A design methodology to maximize the tuning range of the voltage-controlled oscillator (VCO) is presente ... more
Networks
-1.4383 Data-Driven Design of 3GPP Handover Parameters with Bayesian Optimization and Transfer Learning
Authors: Mohamed Benzaghta, Sahar Ammar, David L\'opez-P\'erez, Basem Shihada, Giovanni Geraci | Date: 2025-04-11
Mobility management in dense cellular networks is challenging due to varying user speeds and deployment conditions. Traditional 3GPP handover (HO) schemes, relying on fixed A3-offset and time-to-trigger (TTT) parameters, struggle to balance radio link failures (RLFs) and ping-pongs. We propose a dat ... more
Networks
-1.5434 Quasipolynomial bounds for the corners theorem
Authors: Michael Jaber, Yang P. Liu, Shachar Lovett, Anthony Ostuni, Mehtaab Sawhney | Date: 2025-04-11
Let $G$ be a finite abelian group and $A$ be a subset of $G \times G$ which is corner-free, meaning that there are no $x, y \in G$ and $d \in G \setminus \{0\}$ such that $(x, y)$, $(x+d, y)$, $(x, y+d) \in A$. We prove that \[|A| \le |G|^2 \cdot \exp(-(\log |G|)^{\Omega(1)}).\] As a consequence, we ... more
Math
-1.5553 Successive randomized compression: A randomized algorithm for the compressed MPO-MPS product
Authors: Chris Cama\~no, Ethan N. Epperly, Joel A. Tropp | Date: 2025-04-11
Tensor networks like matrix product states (MPSs) and matrix product operators (MPOs) are powerful tools for representing exponentially large states and operators, with applications in quantum many-body physics, machine learning, numerical analysis, and other areas. In these applications, computing ... more
Math
-2.8271 Compass Control: Multi Object Orientation Control for Text-to-Image Generation
Authors: Rishubh Parihar, Vaibhav Agrawal, Sachidanand VS, R. Venkatesh Babu | Date: 2025-04-11
Existing approaches for controlling text-to-image diffusion models, while powerful, do not allow for explicit 3D object-centric control, such as precise control of object orientation. In this work, we address the problem of multi-object orientation control in text-to-image diffusion models. This ena ... more
T2I
-2.8548 DDT: Decoupled Diffusion Transformer
Authors: Shuai Wang, Zhi Tian, Weilin Huang, Limin Wang | Date: 2025-04-11
Diffusion transformers have demonstrated remarkable generation quality, albeit requiring longer training iterations and numerous inference steps. In each denoising step, diffusion transformers encode the noisy inputs to extract the lower-frequency semantic component and then decode the higher freque ... more
T2I
-3.1666 Confidence Regularized Masked Language Modeling using Text Length
Authors: Seunghyun Ji, Soowon Lee | Date: 2025-04-11
Masked language modeling is a widely used method for learning language representations, where the model predicts a randomly masked word in each input. However, this approach typically considers only a single correct answer during training, ignoring the variety of plausible alternatives that humans m ... more
-3.1678 CMAT: A Multi-Agent Collaboration Tuning Framework for Enhancing Small Language Models
Authors: Xuechen Liang, Meiling Tao, Yinghui Xia, Tianyu Shi, Jun Wang, JingSong Yang | Date: 2025-04-11
Open large language models (LLMs) have significantly advanced the field of natural language processing, showcasing impressive performance across various tasks.Despite the significant advancements in LLMs, their effective operation still relies heavily on human input to accurately guide the dialogue ... more
-3.1711 Bypassing Safety Guardrails in LLMs Using Humor
Authors: Pedro Cisneros-Velarde | Date: 2025-04-11
In this paper, we show it is possible to bypass the safety guardrails of large language models (LLMs) through a humorous prompt including the unsafe request. In particular, our method does not edit the unsafe request and follows a fixed template -- it is simple to implement and does not need additio ... more
-3.1836 A Dataset of Software Bill of Materials for Evaluating SBOM Consumption Tools
Authors: Rio Kishimoto, Tetsuya Kanda, Yuki Manabe, Katsuro Inoue, Shi Qiu, Yoshiki Higo | Date: 2025-04-11
A Software Bill of Materials (SBOM) is becoming an essential tool for effective software dependency management. An SBOM is a list of components used in software, including details such as component names, versions, and licenses. Using SBOMs, developers can quickly identify software components and as ... more
-3.1932 DyDiT++: Dynamic Diffusion Transformers for Efficient Visual Generation
Authors: Wangbo Zhao, Yizeng Han, Jiasheng Tang, Kai Wang, Hao Luo, Yibing Song, Gao Huang, Fan Wang, Yang You | Date: 2025-04-11
Diffusion Transformer (DiT), an emerging diffusion model for visual generation, has demonstrated superior performance but suffers from substantial computational costs. Our investigations reveal that these costs primarily stem from the \emph{static} inference paradigm, which inevitably introduces red ... more
T2I
-3.2493 Can you Finetune your Binoculars? Embedding Text Watermarks into the Weights of Large Language Models
Authors: Fay Elhassan, Niccol\`o Ajroldi, Antonio Orvieto, Jonas Geiping | Date: 2025-04-11
The indistinguishability of AI-generated content from human text raises challenges in transparency and accountability. While several methods exist to watermark models behind APIs, embedding watermark strategies directly into model weights that are later reflected in the outputs of the model is chall ... more
-3.2494 ZIP: An Efficient Zeroth-order Prompt Tuning for Black-box Vision-Language Models
Authors: Seonghwan Park, Jaehyeon Jeong, Yongjun Kim, Jaeho Lee, Namhoon Lee | Date: 2025-04-11
Recent studies have introduced various approaches for prompt-tuning black-box vision-language models, referred to as black-box prompt-tuning (BBPT). While BBPT has demonstrated considerable potential, it is often found that many existing methods require an excessive number of queries (i.e., function ... more
-3.2943 Language-Dependent Political Bias in AI: A Study of ChatGPT and Gemini
Authors: Dogus Yuksel, Mehmet Cem Catalbas, Bora Oc | Date: 2025-04-11
As leading examples of large language models, ChatGPT and Gemini claim to provide accurate and unbiased information, emphasizing their commitment to political neutrality and avoidance of personal bias. This research investigates the political tendency of large language models and the existence of di ... more
-3.3315 Sharpness-Aware Parameter Selection for Machine Unlearning
Authors: Saber Malekmohammadi, Hong kyu Lee, Li Xiong | Date: 2025-04-11
It often happens that some sensitive personal information, such as credit card numbers or passwords, are mistakenly incorporated in the training of machine learning models and need to be removed afterwards. The removal of such information from a trained model is a complex task that needs to partiall ... more
-3.3445 Floralens: a Deep Learning Model for the Portuguese Native Flora
Authors: Ant\'onio Filgueiras, Eduardo R. B. Marques, Lu\'is M. B. Lopes, Miguel Marques, Hugo Silva | Date: 2025-04-11
Machine-learning techniques, especially deep convolutional neural networks, are pivotal for image-based identification of biological species in many Citizen Science platforms. In this paper, we describe the construction of a dataset for the Portuguese native flora based on publicly available researc ... more
-3.3465 IAAO: Interactive Affordance Learning for Articulated Objects in 3D Environments
Authors: Can Zhang, Gim Hee Lee | Date: 2025-04-11
This work presents IAAO, a novel framework that builds an explicit 3D model for intelligent agents to gain understanding of articulated objects in their environment through interaction. Unlike prior methods that rely on task-specific networks and assumptions about movable parts, our IAAO leverages l ... more
-3.3646 Data-driven Optimization and Transfer Learning for Cellular Network Antenna Configurations
Authors: Mohamed Benzaghta, Giovanni Geraci, David L\'opez-P\'erez, Alvaro Valcarce | Date: 2025-04-11
We propose a data-driven approach for large-scale cellular network optimization, using a production cellular network in London as a case study and employing Sionna ray tracing for site-specific channel propagation modeling. We optimize base station antenna tilts and half-power beamwidths, resulting ... more
-3.369 Show and Tell: Visually Explainable Deep Neural Nets via Spatially-Aware Concept Bottleneck Models
Authors: Itay Benou, Tammy Riklin-Raviv | Date: 2025-04-11
Modern deep neural networks have now reached human-level performance across a variety of tasks. However, unlike humans they lack the ability to explain their decisions by showing where and telling what concepts guided them. In this work, we present a unified framework for transforming any vision neu ... more
-3.3749 Reasoning Towards Fairness: Mitigating Bias in Language Models through Reasoning-Guided Fine-Tuning
Authors: Sanchit Kabra, Akshita Jha, Chandan K. Reddy | Date: 2025-04-11
Recent advances in large-scale generative language models have shown that reasoning capabilities can significantly improve model performance across a variety of tasks. However, the impact of reasoning on a model's ability to mitigate stereotypical responses remains largely underexplored. In this wor ... more
-3.3819 Fast Globally Optimal and Geometrically Consistent 3D Shape Matching
Authors: Paul Roetzer, Florian Bernard | Date: 2025-04-11
Geometric consistency, i.e. the preservation of neighbourhoods, is a natural and strong prior in 3D shape matching. Geometrically consistent matchings are crucial for many downstream applications, such as texture transfer or statistical shape modelling. Yet, in practice, geometric consistency is oft ... more
-3.3858 EIDT-V: Exploiting Intersections in Diffusion Trajectories for Model-Agnostic, Zero-Shot, Training-Free Text-to-Video Generation
Authors: Diljeet Jagpal, Xi Chen, Vinay P. Namboodiri | Date: 2025-04-11
Zero-shot, training-free, image-based text-to-video generation is an emerging area that aims to generate videos using existing image-based diffusion models. Current methods in this space require specific architectural changes to image generation models, which limit their adaptability and scalability ... more
-3.3873 Demystifying Language Model Forgetting with Low-rank Example Associations
Authors: Xisen Jin, Xiang Ren | Date: 2025-04-11
Large Language models (LLMs) suffer from forgetting of upstream data when fine-tuned. Despite efforts on mitigating forgetting, few have investigated whether, and how forgotten upstream examples are dependent on newly learned tasks. Insights on such dependencies enable efficient and targeted mitigat ... more
-3.3961 The Method for Storing Patterns in Neural Networks-Memorization and Recall of QR code Patterns-
Authors: Hiroshi Inazawa | Date: 2025-04-11
In this paper, we propose a mechanism for storing complex patterns within a neural network and subsequently recalling them. This model is based on our work published in 2018(Inazawa, 2018), which we have refined and extended in this work. With the recent advancements in deep learning and large langu ... more
-3.3999 AI-Driven Consensus: Modeling Multi-Agent Networks with Long-Range Interactions through path-Laplacian Matrices
Authors: Yusef Ahsini, Bel\'en Reverte, J. Alberto Conejero | Date: 2025-04-11
Extended connectivity in graphs can be analyzed through k-path Laplacian matrices, which permit the capture of long-range interactions in various real-world networked systems such as social, transportation, and multi-agent networks. In this work, we present several alternative methods based on machi ... more
-3.4086 Buffer Centering for bittide Synchronization via Frame Rotation
Authors: Sanjay Lall, Tammo Spalink | Date: 2025-04-11
Maintaining consistent time in distributed systems is a fundamental challenge. The bittide system addresses this by providing logical synchronization through a decentralized control mechanism that observes local buffer occupancies and controls the frequency of an oscillator at each node. A critical ... more
Distributed Systems
-3.4215 Can Large Language Models Replace Data Scientists in Biomedical Research?
Authors: Zifeng Wang, Benjamin Danek, Ziwei Yang, Zheng Chen, Jimeng Sun | Date: 2025-04-11
Data science plays a critical role in biomedical research, but it requires professionals with expertise in coding and medical data analysis. Large language models (LLMs) have shown great potential in supporting medical tasks and performing well in general coding tests. However, existing evaluations ... more
-3.4227 Adaptive Human-Robot Collaborative Missions using Hybrid Task Planning
Authors: Gricel V\'azquez, Alexandros Evangelidis, Sepeedeh Shahbeigi, Simos Gerasimou | Date: 2025-04-11
Producing robust task plans in human-robot collaborative missions is a critical activity in order to increase the likelihood of these missions completing successfully. Despite the broad research body in the area, which considers different classes of constraints and uncertainties, its applicability i ... more
-3.4283 ELOQ: Resources for Enhancing LLM Detection of Out-of-Scope Questions
Authors: Zhiyuan Peng, Jinming Nian, Alexandre Evfimievski, Yi Fang | Date: 2025-04-11
Large Language Models (LLMs) are widely used in Conversational AI systems to generate responses to user inquiries. However, many natural questions lack well-defined answers. While existing studies primarily focus on question types such as false premises, they often overlook out-of-scope questions, w ... more
-3.4633 SketchRef: a Multi-Task Evaluation Benchmark for Sketch Synthesis
Authors: Xingyue Lin, Xingjian Hu, Shuai Peng, Jianhua Zhu, Liangcai Gao | Date: 2025-04-11
Sketching is a powerful artistic technique for capturing essential visual information about real-world objects and has increasingly attracted attention in image synthesis research. However, the field lacks a unified benchmark to evaluate the performance of various synthesis methods. To address this, ... more
-3.4702 Scalable Routing in a City-Scale Wi-Fi Network for Disaster Recovery
Authors: Ziqian Liu, Om Chabra, James Lynch, Chenning Li, Manya Ghobadi, Hari Balakrishnan | Date: 2025-04-11
In this paper, we present a new city-scale decentralized mesh network system suited for disaster recovery and emergencies. When wide-area connectivity is unavailable or significantly degraded, our system, MapMesh, enables static access points and mobile devices equipped with Wi-Fi in a city to route ... more
-3.471 A Novel Massive Random Access in Cell-Free Massive MIMO Systems for High-Speed Mobility with OTFS Modulation
Authors: Yanfeng Hu, Dongming Wang, Xinjiang Xia, Jiamin Li, Pengcheng Zhu, Xiaohu You | Date: 2025-04-11
In the research of next-generation wireless communication technologies, orthogonal time frequency space (OTFS) modulation is emerging as a promising technique for high-speed mobile environments due to its superior efficiency and robustness in doubly selective channels. Additionally, the cell-free ar ... more
-3.4904 Robo-taxi Fleet Coordination at Scale via Reinforcement Learning
Authors: Luigi Tresca, Carolin Schmidt, James Harrison, Filipe Rodrigues, Gioele Zardini, Daniele Gammelli, Marco Pavone | Date: 2025-04-11
Fleets of robo-taxis offering on-demand transportation services, commonly known as Autonomous Mobility-on-Demand (AMoD) systems, hold significant promise for societal benefits, such as reducing pollution, energy consumption, and urban congestion. However, orchestrating these systems at scale remains ... more
-3.5173 DeepGDel: Deep Learning-based Gene Deletion Prediction Framework for Growth-Coupled Production in Genome-Scale Metabolic Models
Authors: Ziwei Yang, Takeyuki Tamura | Date: 2025-04-11
In genome-scale constraint-based metabolic models, gene deletion strategies are crucial for achieving growth-coupled production, where cell growth and target metabolite production are simultaneously achieved. While computational methods for calculating gene deletions have been widely explored and co ... more
-3.5321 Visually Similar Pair Alignment for Robust Cross-Domain Object Detection
Authors: Onkar Krishna, Hiroki Ohashi | Date: 2025-04-11
Domain gaps between training data (source) and real-world environments (target) often degrade the performance of object detection models. Most existing methods aim to bridge this gap by aligning features across source and target domains but often fail to account for visual differences, such as color ... more
-3.5352 Matching and Edge Cover in Temporal Graphs
Authors: Lapo Cioni, Riccardo Dondi, Andrea Marino, Jason Schoeters, Ana Silva | Date: 2025-04-11
Temporal graphs are a special class of graphs for which a temporal component is added to edges, that is, each edge possesses a set of times at which it is available and can be traversed. Many classical problems on graphs can be translated to temporal graphs, and the results may differ. In this paper ... more
-3.5514 Identifying Aspects in Peer Reviews
Authors: Sheng Lu, Ilia Kuznetsov, Iryna Gurevych | Date: 2025-04-11
Peer review is central to academic publishing, but the growing volume of submissions is straining the process. This motivates the development of computational approaches to support peer review. While each review is tailored to a specific paper, reviewers often make assessments according to certain a ... more
-3.5697 Monte Carlo Temperature: a robust sampling strategy for LLM's uncertainty quantification methods
Authors: Nicola Cecere, Andrea Bacciu, Ignacio Fern\'andez Tob\'ias, Amin Mantrach | Date: 2025-04-11
Uncertainty quantification (UQ) in Large Language Models (LLMs) is essential for their safe and reliable deployment, particularly in critical applications where incorrect outputs can have serious consequences. Current UQ methods typically rely on querying the model multiple times using non-zero temp ... more
-3.5823 Bridging Queries and Tables through Entities in Table Retrieval
Authors: Da Li, Keping Bi, Jiafeng Guo, Xueqi Cheng | Date: 2025-04-11
Table retrieval is essential for accessing information stored in structured tabular formats; however, it remains less explored than text retrieval. The content of the table primarily consists of phrases and words, which include a large number of entities, such as time, locations, persons, and organi ... more
-3.5827 RuOpinionNE-2024: Extraction of Opinion Tuples from Russian News Texts
Authors: Natalia Loukachevitch, Natalia Tkachenko, Anna Lapanitsyna, Mikhail Tikhomirov, Nicolay Rusnachenko | Date: 2025-04-11
In this paper, we introduce the Dialogue Evaluation shared task on extraction of structured opinions from Russian news texts. The task of the contest is to extract opinion tuples for a given sentence; the tuples are composed of a sentiment holder, its target, an expression and sentiment from the hol ... more
-3.5946 Enhancing Virtual Try-On with Synthetic Pairs and Error-Aware Noise Scheduling
Authors: Nannan Li, Kevin J. Shih, Bryan A. Plummer | Date: 2025-04-11
Given an isolated garment image in a canonical product view and a separate image of a person, the virtual try-on task aims to generate a new image of the person wearing the target garment. Prior virtual try-on works face two major challenges in achieving this goal: a) the paired (human, garment) tra ... more
-3.5985 GTS-LUM: Reshaping User Behavior Modeling with LLMs in Telecommunications Industry
Authors: Liu Shi, Tianwu Zhou, Wei Xu, Li Liu, Zhexin Cui, Shaoyi Liang, Haoxing Niu, Yichong Tian, Jianwei Guo | Date: 2025-04-11
As telecommunication service providers shifting their focus to analyzing user behavior for package design and marketing interventions, a critical challenge lies in developing a unified, end-to-end framework capable of modeling long-term and periodic user behavior sequences with diverse time granular ... more
-3.5991 Physics-informed KAN PointNet: Deep learning for simultaneous solutions to inverse problems in incompressible flow on numerous irregular geometries
Authors: Ali Kashefi, Tapan Mukerji | Date: 2025-04-11
Kolmogorov-Arnold Networks (KANs) have gained attention as a promising alternative to traditional Multilayer Perceptrons (MLPs) for deep learning applications in computational physics, especially within the framework of physics-informed neural networks (PINNs). Physics-informed Kolmogorov-Arnold Net ... more
-3.6025 Understanding Users' Security and Privacy Concerns and Attitudes Towards Conversational AI Platforms
Authors: Mutahar Ali, Arjun Arunasalam, Habiba Farrukh | Date: 2025-04-11
The widespread adoption of conversational AI platforms has introduced new security and privacy risks. While these risks and their mitigation strategies have been extensively researched from a technical perspective, users' perceptions of these platforms' security and privacy remain largely unexplored ... more
-3.6048 EDIT: Enhancing Vision Transformers by Mitigating Attention Sink through an Encoder-Decoder Architecture
Authors: Wenfeng Feng, Guoying Sun | Date: 2025-04-11
In this paper, we propose EDIT (Encoder-Decoder Image Transformer), a novel architecture designed to mitigate the attention sink phenomenon observed in Vision Transformer models. Attention sink occurs when an excessive amount of attention is allocated to the [CLS] token, distorting the model's abili ... more
-3.6067 Mixed-Precision in High-Order Methods: the Impact of Floating-Point Precision on the ADER-DG Algorithm
Authors: Marc Marot-Lassauzaie, Michael Bader | Date: 2025-04-11
We present a mixed-precision implementation of the high-order discontinuous Galerkin method with ADER time stepping (ADER-DG) for solving hyperbolic systems of partial differential equations (PDEs) in the hyperbolic PDE engine ExaHyPE. The implementation provides a simple API extension for specifyin ... more
-3.6081 Understanding Machine Unlearning Through the Lens of Mode Connectivity
Authors: Jiali Cheng, Hadi Amiri | Date: 2025-04-11
Machine Unlearning aims to remove undesired information from trained models without requiring full retraining from scratch. Despite recent advancements, their underlying loss landscapes and optimization dynamics received less attention. In this paper, we investigate and analyze machine unlearning th ... more
-3.6236 FJ-MM: The Friedkin-Johnsen Opinion Dynamics Model with Memory and Higher-Order Neighbors
Authors: Roberta Raineri, Lorenzo Zino, Anton Proskurnikov | Date: 2025-04-11
The Friedkin-Johnsen (FJ) model has been extensively explored and validated, spanning applications in social science, systems and control, game theory, and algorithmic research. In this paper, we introduce an advanced generalization of the FJ model, termed FJ-MM which incorporates both memory effect ... more
-3.6305 Interactive Visualization Recommendation with Hier-SUCB
Authors: Songwen Hu, Ryan A. Rossi, Tong Yu, Junda Wu, Handong Zhao, Sungchul Kim, Shuai Li | Date: 2025-04-11
Visualization recommendation aims to enable rapid visual analysis of massive datasets. In real-world scenarios, it is essential to quickly gather and comprehend user preferences to cover users from diverse backgrounds, including varying skill levels and analytical tasks. Previous approaches to perso ... more
-3.6366 Bridging the Gap Between Preference Alignment and Machine Unlearning
Authors: Xiaohua Feng, Yuyuan Li, Huwei Ji, Jiaming Zhang, Li Zhang, Tianyu Du, Chaochao Chen | Date: 2025-04-11
Despite advances in Preference Alignment (PA) for Large Language Models (LLMs), mainstream methods like Reinforcement Learning with Human Feedback (RLHF) face notable challenges. These approaches require high-quality datasets of positive preference examples, which are costly to obtain and computatio ... more
-3.6412 Recasting Arrow's Impossibility Theorem as G\"odelian Incomputability
Authors: Ori Livson, Mikhail Prokopenko | Date: 2025-04-11
Incomputability results in formal logic and the Theory of Computation (i.e., incompleteness and undecidability) have deep implications for the foundations of mathematics and computer science. Likewise, Social Choice Theory, a branch of Welfare Economics, contains several impossibility results that p ... more
-3.648 Lugha-Llama: Adapting Large Language Models for African Languages
Authors: Happy Buzaaba, Alexander Wettig, David Ifeoluwa Adelani, Christiane Fellbaum | Date: 2025-04-11
Large language models (LLMs) have achieved impressive results in a wide range of natural language applications. However, they often struggle to recognize low-resource languages, in particular African languages, which are not well represented in large training corpora. In this paper, we consider how ... more
-3.6528 A Flexible Large Language Models Guardrail Development Methodology Applied to Off-Topic Prompt Detection
Authors: Gabriel Chua, Shing Yee Chan, Shaun Khoo | Date: 2025-04-11
Large Language Models (LLMs) are prone to off-topic misuse, where users may prompt these models to perform tasks beyond their intended scope. Current guardrails, which often rely on curated examples or custom classifiers, suffer from high false-positive rates, limited adaptability, and the impractic ... more
-3.6548 AuroraCap: Efficient, Performant Video Detailed Captioning and a New Benchmark
Authors: Wenhao Chai, Enxin Song, Yilun Du, Chenlin Meng, Vashisht Madhavan, Omer Bar-Tal, Jenq-Neng Hwang, Saining Xie, Christopher D. Manning | Date: 2025-04-11
Video detailed captioning is a key task which aims to generate comprehensive and coherent textual descriptions of video content, benefiting both video understanding and generation. In this paper, we propose AuroraCap, a video captioner based on a large multimodal model. We follow the simplest archit ... more
-3.6557 Task-Based Tensor Computations on Modern GPUs
Authors: Rohan Yadav, Michael Garland, Alex Aiken, Michael Bauer | Date: 2025-04-11
Domain-specific, fixed-function units are becoming increasingly common in modern processors. As the computational demands of applications evolve, the capabilities and programming interfaces of these fixed-function units continue to change. NVIDIA's Hopper GPU architecture contains multiple fixed-fun ... more
-3.6596 Foundation Model for Composite Microstructures: Reconstruction, Stiffness, and Nonlinear Behavior Prediction
Authors: Ting-Ju Wei, Chuin-Shan Chen | Date: 2025-04-11
The rapid advancement of machine learning has unlocked numerous opportunities for materials science, particularly in accelerating the design and analysis of materials. However, a significant challenge lies in the scarcity and high cost of obtaining high-quality materials datasets. While foundation m ... more
-3.6611 The Zero Body Problem: Probing LLM Use of Sensory Language
Authors: Rebecca M. M. Hicke, Sil Hamilton, David Mimno | Date: 2025-04-11
Sensory language expresses embodied experiences ranging from taste and sound to excitement and stomachache. This language is of interest to scholars from a wide range of domains including robotics, narratology, linguistics, and cognitive science. In this work, we explore whether language models, whi ... more
-3.662 Flexible Graph Similarity Computation With A Proactive Optimization Strategy
Authors: Zhouyang Liu, Ning Liu, Yixin Chen, Jiezhong He, Dongsheng Li | Date: 2025-04-11
Graph Edit Distance (GED) is an important similarity measure in graph retrieval, which quantifies the minimum cost of transforming one graph into another through edit operations, and offers flexibility by allowing customizable operation costs. Recent learning-based approaches approximate GEDs with t ... more
-3.6694 A Survey on Personalized and Pluralistic Preference Alignment in Large Language Models
Authors: Zhouhang Xie, Junda Wu, Yiran Shen, Yu Xia, Xintong Li, Aaron Chang, Ryan Rossi, Sachin Kumar, Bodhisattwa Prasad Majumder, Jingbo Shang, Prithviraj Ammanabrolu, Julian McAuley | Date: 2025-04-11
Personalized preference alignment for large language models (LLMs), the process of tailoring LLMs to individual users' preferences, is an emerging research direction spanning the area of NLP and personalization. In this survey, we present an analysis of works on personalized alignment and modeling f ... more
-3.6749 A Sober Look at Progress in Language Model Reasoning: Pitfalls and Paths to Reproducibility
Authors: Andreas Hochlehnert, Hardik Bhatnagar, Vishaal Udandarao, Samuel Albanie, Ameya Prabhu, Matthias Bethge | Date: 2025-04-11
Reasoning has emerged as the next major frontier for language models (LMs), with rapid advances from both academic and industrial labs. However, this progress often outpaces methodological rigor, with many evaluations relying on benchmarking practices that lack transparency, robustness, or statistic ... more
-3.6759 DeduCE: Deductive Consistency as a Framework to Evaluate LLM Reasoning
Authors: Atharva Pandey, Kshitij Dubey, Rahul Sharma, Amit Sharma | Date: 2025-04-11
Despite great performance on Olympiad-level reasoning problems, frontier large language models can still struggle on high school math when presented with novel problems outside standard benchmarks. Going beyond final accuracy, we propose a deductive consistency metric to analyze chain-of-thought out ... more
-3.6759 LUDO: Low-Latency Understanding of Deformable Objects using Point Cloud Occupancy Functions
Authors: Pit Henrich, Franziska Mathis-Ullrich, Paul Maria Scheikl | Date: 2025-04-11
Accurately determining the shape of objects and the location of their internal structures within deformable objects is crucial for medical tasks that require precise targeting, such as robotic biopsies. We introduce LUDO, a method for accurate low-latency understanding of deformable objects. LUDO re ... more
-3.6882 Estimation of embedding vectors in high dimensions
Authors: Golara Ahmadi Azar, Melika Emami, Alyson Fletcher, Sundeep Rangan | Date: 2025-04-11
Embeddings are a basic initial feature extraction step in many machine learning models, particularly in natural language processing. An embedding attempts to map data tokens to a low-dimensional space where similar tokens are mapped to vectors that are close to one another by some metric in the embe ... more
-3.6949 PathSegDiff: Pathology Segmentation using Diffusion model representations
Authors: Sachin Kumar Danisetty, Alexandros Graikos, Srikar Yellapragada, Dimitris Samaras | Date: 2025-04-11
Image segmentation is crucial in many computational pathology pipelines, including accurate disease diagnosis, subtyping, outcome, and survivability prediction. The common approach for training a segmentation model relies on a pre-trained feature extractor and a dataset of paired image and mask anno ... more
-3.7021 Robust Classification with Noisy Labels Based on Posterior Maximization
Authors: Nicola Novello, Andrea M. Tonello | Date: 2025-04-11
Designing objective functions robust to label noise is crucial for real-world classification algorithms. In this paper, we investigate the robustness to label noise of an $f$-divergence-based class of objective functions recently proposed for supervised classification, herein referred to as $f$-PML. ... more
-3.7163 Domain-Specific Pruning of Large Mixture-of-Experts Models with Few-shot Demonstrations
Authors: Zican Dong, Han Peng, Peiyu Liu, Wayne Xin Zhao, Dong Wu, Feng Xiao, Zhifeng Wang | Date: 2025-04-11
Mixture-of-Experts (MoE) models achieve a favorable trade-off between performance and inference efficiency by activating only a subset of experts. However, the memory overhead of storing all experts remains a major limitation, especially in large-scale MoE models such as DeepSeek-R1 (671B). In this ... more
-3.7178 A Comparison of Deep Learning Methods for Cell Detection in Digital Cytology
Authors: Marco Acerbis, Nata\v{s}a Sladoje, Joakim Lindblad | Date: 2025-04-11
Accurate and efficient cell detection is crucial in many biomedical image analysis tasks. We evaluate the performance of several Deep Learning (DL) methods for cell detection in Papanicolaou-stained cytological Whole Slide Images (WSIs), focusing on accuracy of predictions and computational efficien ... more
-3.7354 ThoughtProbe: Classifier-Guided Thought Space Exploration Leveraging LLM Intrinsic Reasoning
Authors: Zijian Wang, Chang Xu | Date: 2025-04-11
Pre-trained large language models (LLMs) have been demonstrated to possess intrinsic reasoning capabilities that can emerge naturally when expanding the response space. However, the neural representation mechanisms underlying these intrinsic capabilities and approaches for their optimal utilization ... more
-3.7356 Subjective Visual Quality Assessment for High-Fidelity Learning-Based Image Compression
Authors: Mohsen Jenadeleh, Jon Sneyers, Panqi Jia, Shima Mohammadi, Joao Ascenso, Dietmar Saupe | Date: 2025-04-11
Learning-based image compression methods have recently emerged as promising alternatives to traditional codecs, offering improved rate-distortion performance and perceptual quality. JPEG AI represents the latest standardized framework in this domain, leveraging deep neural networks for high-fidelity ... more
-3.7374 Medical-GAT: Cancer Document Classification Leveraging Graph-Based Residual Network for Scenarios with Limited Data
Authors: Elias Hossain, Tasfia Nuzhat, Shamsul Masum, Shahram Rahimi, Noorbakhsh Amiri Golilarz | Date: 2025-04-11
Accurate classification of cancer-related medical abstracts is crucial for healthcare management and research. However, obtaining large, labeled datasets in the medical domain is challenging due to privacy concerns and the complexity of clinical data. This scarcity of annotated data impedes the deve ... more
-3.7439 Parallel GPU-Enabled Algorithms for SpGEMM on Arbitrary Semirings with Hybrid Communication
Authors: Thomas McFarland, Julian Bellavita, Giulia Guidi | Date: 2025-04-11
Sparse General Matrix Multiply (SpGEMM) is key for various High-Performance Computing (HPC) applications such as genomics and graph analytics. Using the semiring abstraction, many algorithms can be formulated as SpGEMM, allowing redefinition of addition, multiplication, and numeric types. Today larg ... more
-3.7462 Data-Driven Reachability with Scenario Optimization and the Holdout Method
Authors: Elizabeth Dietrich, Rosalyn Devonport, Stephen Tu, Murat Arcak | Date: 2025-04-11
Reachability analysis is an important method in providing safety guarantees for systems with unknown or uncertain dynamics. Due to the computational intractability of exact reachability analysis for general nonlinear, high-dimensional systems, recent work has focused on the use of probabilistic meth ... more
-3.747 Sequential-NIAH: A Needle-In-A-Haystack Benchmark for Extracting Sequential Needles from Long Contexts
Authors: Yifei Yu, Qian-Wen Zhang, Lingfeng Qiao, Di Yin, Fang Li, Jie Wang, Zengxi Chen, Suncong Zheng, Xiaolong Liang, Xing Sun | Date: 2025-04-11
Evaluating the ability of large language models (LLMs) to handle extended contexts is critical, particularly for retrieving information relevant to specific queries embedded within lengthy inputs. We introduce Sequential-NIAH, a benchmark specifically designed to evaluate the capability of LLMs to e ... more
-3.7494 Analog Computing with Microwave Networks
Authors: Matteo Nerini, Bruno Clerckx | Date: 2025-04-11
Analog computing has been recently revived due to its potential for energy-efficient and highly parallel computations. In this paper, we investigate analog computers that linearly process microwave signals, named microwave linear analog computers (MiLACs), and their applications in signal processing ... more
-3.7516 Classifying the Unknown: In-Context Learning for Open-Vocabulary Text and Symbol Recognition
Authors: Tom Simon, William Mocaer, Pierrick Tranouez, Clement Chatelain, Thierry Paquet | Date: 2025-04-11
We introduce Rosetta, a multimodal model that leverages Multimodal In-Context Learning (MICL) to classify sequences of novel script patterns in documents by leveraging minimal examples, thus eliminating the need for explicit retraining. To enhance contextual learning, we designed a dataset generatio ... more
-3.7642 The geometry of covering codes in the sum-rank metric
Authors: Matteo Bonini, Martino Borello, Eimear Byrne | Date: 2025-04-11
We introduce the concept of a sum-rank saturating system and outline its correspondence to a covering properties of a sum-rank metric code. We consider the problem of determining the shortest sum-rank-$\rho$-saturating systems of a fixed dimension, which is equivalent to the covering problem in the ... more
-3.7691 ORAL: Prompting Your Large-Scale LoRAs via Conditional Recurrent Diffusion
Authors: Rana Muhammad Shahroz Khan, Dongwen Tang, Pingzhi Li, Kai Wang, Tianlong Chen | Date: 2025-04-11
Parameter generation has emerged as a novel paradigm for neural network development, offering an alternative to traditional neural network training by synthesizing high-quality model weights directly. In the context of Low-Rank Adaptation (LoRA) for evolving ($\textit{i.e.}$, constantly updated) lar ... more
-3.7693 Resurrecting Socrates in the Age of AI: A Study Protocol for Evaluating a Socratic Tutor to Support Research Question Development in Higher Education
Authors: Ben Degen | Date: 2025-04-11
Formulating research questions is a foundational yet challenging academic skill, one that generative AI systems often oversimplify by offering instant answers at the expense of student reflection. This protocol lays out a study grounded in constructivist learning theory to evaluate a novel AI-based ... more
-3.7698 Beyond the Hype: Embeddings vs. Prompting for Multiclass Classification Tasks
Authors: Marios Kokkodis, Richard Demsyn-Jones, Vijay Raghavan | Date: 2025-04-11
Are traditional classification approaches irrelevant in this era of AI hype? We show that there are multiclass classification problems where predictive models holistically outperform LLM prompt-based frameworks. Given text and images from home-service project descriptions provided by Thumbtack custo ... more
-3.7718 Patch Matters: Training-free Fine-grained Image Caption Enhancement via Local Perception
Authors: Ruotian Peng, Haiying He, Yake Wei, Yandong Wen, Di Hu | Date: 2025-04-11
High-quality image captions play a crucial role in improving the performance of cross-modal applications such as text-to-image generation, text-to-video generation, and text-image retrieval. To generate long-form, high-quality captions, many recent studies have employed multimodal large language mod ... more
-3.7804 Neural Motion Simulator: Pushing the Limit of World Models in Reinforcement Learning
Authors: Chenjie Hao, Weyl Lu, Yifan Xu, Yubei Chen | Date: 2025-04-11
An embodied system must not only model the patterns of the external world but also understand its own motion dynamics. A motion dynamic model is essential for efficient skill acquisition and effective planning. In this work, we introduce the neural motion simulator (MoSim), a world model that predic ... more
-3.7814 Scalable Robust Bayesian Co-Clustering with Compositional ELBOs
Authors: Ashwin Vinod, Chandrajit Bajaj | Date: 2025-04-11
Co-clustering exploits the duality of instances and features to simultaneously uncover meaningful groups in both dimensions, often outperforming traditional clustering in high-dimensional or sparse data settings. Although recent deep learning approaches successfully integrate feature learning and cl ... more
-3.7875 DiffusionCom: Structure-Aware Multimodal Diffusion Model for Multimodal Knowledge Graph Completion
Authors: Wei Huang, Meiyu Liang, Peining Li, Xu Hou, Yawen Li, Junping Du, Zhe Xue, Zeli Guan | Date: 2025-04-11
Most current MKGC approaches are predominantly based on discriminative models that maximize conditional likelihood. These approaches struggle to efficiently capture the complex connections in real-world knowledge graphs, thereby limiting their overall performance. To address this issue, we propose a ... more
-3.7891 Design and use of devices to assist movement of the upper limb: review of the literature
Authors: Charlotte Le Goff (CAMIN), Pauline Coignard (CAMIN), Christine Azevedo-Coste (CAMIN), Franck Geffard (DIN), Charles Fattal (CAMIN) | Date: 2025-04-11
This article explores assistive devices for upper limb movement in people with disabilities through a systematic review based on the PRISMA methodology. The studied devices encompass technologies ranging from orthoses to advanced robotics, aiming to compensate for or supplement motor impairments. Th ... more
-3.7926 SkillWeaver: Web Agents can Self-Improve by Discovering and Honing Skills
Authors: Boyuan Zheng, Michael Y. Fatemi, Xiaolong Jin, Zora Zhiruo Wang, Apurva Gandhi, Yueqi Song, Yu Gu, Jayanth Srinivasa, Gaowen Liu, Graham Neubig, Yu Su | Date: 2025-04-11
To survive and thrive in complex environments, humans have evolved sophisticated self-improvement mechanisms through environment exploration, hierarchical abstraction of experiences into reuseable skills, and collaborative construction of an ever-growing skill repertoire. Despite recent advancements ... more
-3.7963 Towards Calibration Enhanced Network by Inverse Adversarial Attack
Authors: Yupeng Cheng, Zi Pong Lim, Sarthak Ketanbhai Modi, Yon Shin Teo, Yushi Cao, Shang-Wei Lin | Date: 2025-04-11
Test automation has become increasingly important as the complexity of both design and content in Human Machine Interface (HMI) software continues to grow. Current standard practice uses Optical Character Recognition (OCR) techniques to automatically extract textual information from HMI screens for ... more
-3.7964 Rethinking RoPE: A Mathematical Blueprint for N-dimensional Positional Encoding
Authors: Haiping Liu, Hongpeng Zhou | Date: 2025-04-11
Rotary Position Embedding (RoPE) is widely adopted in Transformers due to its ability to encode relative positions with high efficiency and extrapolation capability. However, existing RoPE variants lack a unified theoretical foundation, especially in higher dimensions. In this paper, we propose a sy ... more
LLMs
-3.8041 Thin Coalgebraic Behaviours Are Inductive
Authors: Anton Chernev, Corina C\^irstea, Helle Hvid Hansen, Clemens Kupke | Date: 2025-04-11
Coalgebras for analytic functors uniformly model graph-like systems where the successors of a state may admit certain symmetries. Examples of successor structure include ordered tuples, cyclic lists and multisets. Motivated by goals in automata-based verification and results on thin trees, we introd ... more
-3.807 Efficient Ranking Function-Based Termination Analysis with Bi-Directional Feedback
Authors: Yasmin Sarita, Avaljot Singh, Shaurya Gomber, Gagandeep Singh, Mahesh Vishwanathan | Date: 2025-04-11
Synthesizing ranking functions is a common technique for proving the termination of loops. A ranking function must be bounded and decrease by a specified amount with each iteration for all reachable program states. However, the set of reachable program states is often unknown, and loop invariants ar ... more
-3.8118 PosterMaker: Towards High-Quality Product Poster Generation with Accurate Text Rendering
Authors: Yifan Gao, Zihang Lin, Chuanbin Liu, Min Zhou, Tiezheng Ge, Bo Zheng, Hongtao Xie | Date: 2025-04-11
Product posters, which integrate subject, scene, and text, are crucial promotional tools for attracting customers. Creating such posters using modern image generation methods is valuable, while the main challenge lies in accurately rendering text, especially for complex writing systems like Chinese, ... more
-3.8144 Several new infinite families of NMDS codes with arbitrary dimensions supporting $t$-designs
Authors: Yaozong Zhang, Dabin Zheng, Xiaoqiang Wang, Wei Lu | Date: 2025-04-11
Near maximum distance separable (NMDS) codes, where both the code and its dual are almost maximum distance separable, play pivotal roles in combinatorial design theory and cryptographic applications. Despite progress in fixed dimensions (e.g., dimension 4 codes by Ding and Tang \cite{Ding2020}), con ... more
-3.8161 Introducing the Arm-membench Throughput Benchmark
Authors: Cyrill Burth, Markus Velten, Robert Sch\"one | Date: 2025-04-11
Application performance of modern day processors is often limited by the memory subsystem rather than actual compute capabilities. Therefore, data throughput specifications play a key role in modeling application performance and determining possible bottlenecks. However, while peak instruction throu ... more
Hardware
-3.8196 Integrating Cognitive Processing Signals into Language Models: A Review of Advances, Applications and Future Directions
Authors: Angela Lopez-Cardona, Sebastian Idesis, Ioannis Arapakis | Date: 2025-04-11
Recently, the integration of cognitive neuroscience in Natural Language Processing (NLP) has gained significant attention. This article provides a critical and timely overview of recent advancements in leveraging cognitive signals, particularly Eye-tracking (ET) signals, to enhance Language Models ( ... more
-3.8232 Topic-aware Most Influential Community Search in Social Networks
Authors: Long Teng, Yanhao Wang, Zhe Lin, Fei Yu | Date: 2025-04-11
Influential community search (ICS) finds a set of densely connected and high-impact vertices from a social network. Although great effort has been devoted to ICS problems, most existing methods do not consider how relevant the influential community found is to specific topics. A few attempts at topi ... more
-3.828 InteractRank: Personalized Web-Scale Search Pre-Ranking with Cross Interaction Features
Authors: Sujay Khandagale, Bhawna Juneja, Prabhat Agarwal, Aditya Subramanian, Jaewon Yang, Yuting Wang | Date: 2025-04-11
Modern search systems use a multi-stage architecture to deliver personalized results efficiently. Key stages include retrieval, pre-ranking, full ranking, and blending, which refine billions of items to top selections. The pre-ranking stage, vital for scoring and filtering hundreds of thousands of i ... more
-3.831 Assessing Computer Science Student Attitudes Towards AI Ethics and Policy
Authors: James Weichert, Dayoung Kim, Qin Zhu, Junghwan Kim, Hoda Eldardiry | Date: 2025-04-11
As artificial intelligence (AI) grows in popularity and importance-both as a domain within broader computing research and in society at large-increasing focus will need to be paid to the ethical governance of this emerging technology. The attitudes and competencies with respect to AI ethics and poli ... more
-3.831 Beyond the Hype: A dispassionate look at vision-language models in medical scenario
Authors: Yang Nan, Huichi Zhou, Xiaodan Xing, Guang Yang | Date: 2025-04-11
Recent advancements in Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities across diverse tasks, garnering significant attention in AI communities. However, their performance and reliability in specialized domains such as medicine remain insufficiently assessed. In particu ... more
-3.8318 RAGME: Retrieval Augmented Video Generation for Enhanced Motion Realism
Authors: Elia Peruzzo, Dejia Xu, Xingqian Xu, Humphrey Shi, Nicu Sebe | Date: 2025-04-11
Video generation is experiencing rapid growth, driven by advances in diffusion models and the development of better and larger datasets. However, producing high-quality videos remains challenging due to the high-dimensional data and the complexity of the task. Recent efforts have primarily focused o ... more
-3.8348 Zero-Shot Image-Based Large Language Model Approach to Road Pavement Monitoring
Authors: Shuoshuo Xu, Kai Zhao, James Loney, Zili Li, Andrea Visentin | Date: 2025-04-11
Effective and rapid evaluation of pavement surface condition is critical for prioritizing maintenance, ensuring transportation safety, and minimizing vehicle wear and tear. While conventional manual inspections suffer from subjectivity, existing machine learning-based methods are constrained by thei ... more
-3.8369 Market, power, gift, and concession economies: Comparison using four-mode primitive network models
Authors: Takeshi Kato, Junichi Miyakoshi, Misa Owa, Ryuji Mine | Date: 2025-04-11
Reducing wealth inequality is a global challenge, and the problems of capitalism stem from the enclosure of the commons and the breakdown of the community. According to previous studies by Polanyi, Karatani, and Graeber, economic modes can be divided into capitalist market economy (enclosure and exc ... more
-3.8429 Two by Two: Learning Multi-Task Pairwise Objects Assembly for Generalizable Robot Manipulation
Authors: Yu Qi, Yuanchen Ju, Tianming Wei, Chi Chu, Lawson L. S. Wong, Huazhe Xu | Date: 2025-04-11
3D assembly tasks, such as furniture assembly and component fitting, play a crucial role in daily life and represent essential capabilities for future home robots. Existing benchmarks and datasets predominantly focus on assembling geometric fragments or factory parts, which fall short in addressing ... more
-3.845 PRMBench: A Fine-grained and Challenging Benchmark for Process-Level Reward Models
Authors: Mingyang Song, Zhaochen Su, Xiaoye Qu, Jiawei Zhou, Yu Cheng | Date: 2025-04-11
Process-level Reward Models (PRMs) are crucial for complex reasoning and decision-making tasks, where each intermediate step plays an important role in the reasoning process. Since language models are prone to various types of errors during the reasoning process, PRMs are required to possess nuanced ... more
-3.8455 Towards LLMs Robustness to Changes in Prompt Format Styles
Authors: Lilian Ngweta, Kiran Kate, Jason Tsay, Yara Rizk | Date: 2025-04-11
Large language models (LLMs) have gained popularity in recent years for their utility in various applications. However, they are sensitive to non-semantic changes in prompt formats, where small changes in the prompt format can lead to significant performance fluctuations. In the literature, this pro ... more
-3.853 HGMamba: Enhancing 3D Human Pose Estimation with a HyperGCN-Mamba Network
Authors: Hu Cui, Tessai Hayama | Date: 2025-04-11
3D human pose lifting is a promising research area that leverages estimated and ground-truth 2D human pose data for training. While existing approaches primarily aim to enhance the performance of estimated 2D poses, they often struggle when applied to ground-truth 2D pose data. We observe that achie ... more
-3.8569 Exploiting Meta-Learning-based Poisoning Attacks for Graph Link Prediction
Authors: Mingchen Li, Di Zhuang, Keyu Chen, Dumindu Samaraweera, Morris Chang | Date: 2025-04-11
Link prediction in graph data utilizes various algorithms and machine learning/deep learning models to predict potential relationships between graph nodes. This technique has found widespread use in numerous real-world applications, including recommendation systems, community networks, and biologica ... more
-3.8579 The Essence of Contextual Understanding in Theory of Mind: A Study on Question Answering with Story Characters
Authors: Chulun Zhou, Qiujing Wang, Mo Yu, Xiaoqian Yue, Rui Lu, Jiangnan Li, Yifan Zhou, Shunchi Zhang, Jie Zhou, Wai Lam | Date: 2025-04-11
Theory-of-Mind (ToM) is a fundamental psychological capability that allows humans to understand and interpret the mental states of others. Humans infer others' thoughts by integrating causal cues and indirect clues from broad contextual information, often derived from past interactions. In other wor ... more
-3.8641 FeedbackEval: A Benchmark for Evaluating Large Language Models in Feedback-Driven Code Repair Tasks
Authors: Dekun Dai, MingWei Liu, Anji Li, Jialun Cao, Yanlin Wang, Chong Wang, Xin Peng, Zibin Zheng | Date: 2025-04-11
Code repair is a fundamental task in software development, facilitating efficient bug resolution and software maintenance. Although large language models (LLMs) have demonstrated considerable potential in automated code repair, their ability to comprehend and effectively leverage diverse types of fe ... more
-3.8671 User-Centered Insights into Assistive Navigation Technologies for Individuals with Visual Impairment
Authors: Iman Soltani, Johnaton Schofield, Mehran Madani, Daniel Kish, Parisa Emami-Naeini | Date: 2025-04-11
Navigational challenges significantly impact the independence and mobility of Individuals with Visual Impairment (IVI). While numerous assistive technologies exist, their adoption remains limited due to usability challenges, financial constraints, and a lack of alignment with user needs. This study ... more
-3.8673 Membrane: Accelerating Database Analytics with Bank-Level DRAM-PIM Filtering
Authors: Akhil Shekar, Kevin Gaffney, Martin Prammer, Khyati Kiyawat, Lingxi Wu, Helena Caminal, Zhenxing Fan, Yimin Gao, Ashish Venkat, Jos\'e F. Mart\'inez, Jignesh Patel, Kevin Skadron | Date: 2025-04-11
In-memory database query processing frequently involves substantial data transfers between the CPU and memory, leading to inefficiencies due to Von Neumann bottleneck. Processing-in-Memory (PIM) architectures offer a viable solution to alleviate this bottleneck. In our study, we employ a commonly us ... more
-3.8682 Human-like compositional learning of visually-grounded concepts using synthetic environments
Authors: Zijun Lin, M Ganesh Kumar, Cheston Tan | Date: 2025-04-11
The compositional structure of language enables humans to decompose complex phrases and map them to novel visual concepts, showcasing flexible intelligence. While several algorithms exhibit compositionality, they fail to elucidate how humans learn to compose concept classes and ground visual cues th ... more
-3.8711 LLM-A*: Large Language Model Enhanced Incremental Heuristic Search on Path Planning
Authors: Silin Meng, Yiwei Wang, Cheng-Fu Yang, Nanyun Peng, Kai-Wei Chang | Date: 2025-04-11
Path planning is a fundamental scientific problem in robotics and autonomous navigation, requiring the derivation of efficient routes from starting to destination points while avoiding obstacles. Traditional algorithms like A* and its variants are capable of ensuring path validity but suffer from si ... more
-3.8716 CAI: An Open, Bug Bounty-Ready Cybersecurity AI
Authors: V\'ictor Mayoral-Vilches, Luis Javier Navarrete-Lozano, Mar\'ia Sanz-G\'omez, Lidia Salas Espejo, Marti\~no Crespo-\'Alvarez, Francisco Oca-Gonzalez, Francesco Balassone, Alfonso Glera-Pic\'on, Unai Ayucar-Carbajo, Jon Ander Ruiz-Alcalde, Stefan Rass, Martin Pinzger, Endika Gil-Uriarte | Date: 2025-04-11
By 2028 most cybersecurity actions will be autonomous, with humans teleoperating. We present the first classification of autonomy levels in cybersecurity and introduce Cybersecurity AI (CAI), an open-source framework that democratizes advanced security testing through specialized AI agents. Through ... more
-3.874 HoTPP Benchmark: Are We Good at the Long Horizon Events Forecasting?
Authors: Ivan Karpukhin, Foma Shipilov, Andrey Savchenko | Date: 2025-04-11
Forecasting multiple future events within a given time horizon is essential for applications in finance, retail, social networks, and healthcare. Marked Temporal Point Processes (MTPP) provide a principled framework to model both the timing and labels of events. However, most existing research focus ... more
-3.8746 Data Augmentation for Fake Reviews Detection in Multiple Languages and Multiple Domains
Authors: Ming Liu, Massimo Poesio | Date: 2025-04-11
With the growth of the Internet, buying habits have changed, and customers have become more dependent on the online opinions of other customers to guide their purchases. Identifying fake reviews thus became an important area for Natural Language Processing (NLP) research. However, developing high-pe ... more
-3.8755 A Neuro-inspired Interpretation of Unlearning in Large Language Models through Sample-level Unlearning Difficulty
Authors: Xiaohua Feng, Yuyuan Li, Chengye Wang, Junlin Liu, Li Zhang, Chaochao Chen | Date: 2025-04-11
Driven by privacy protection laws and regulations, unlearning in Large Language Models (LLMs) is gaining increasing attention. However, current research often neglects the interpretability of the unlearning process, particularly concerning sample-level unlearning difficulty. Existing studies typical ... more
-3.8755 LLM-IFT: LLM-Powered Information Flow Tracking for Secure Hardware
Authors: Nowfel Mashnoor, Mohammad Akyash, Hadi Kamali, Kimia Azar | Date: 2025-04-11
As modern hardware designs grow in complexity and size, ensuring security across the confidentiality, integrity, and availability (CIA) triad becomes increasingly challenging. Information flow tracking (IFT) is a widely-used approach to tracing data propagation, identifying unauthorized activities t ... more
-3.881 Do Reasoning Models Show Better Verbalized Calibration?
Authors: Qingcheng Zeng, Weihao Xuan, Leyang Cui, Rob Voigt | Date: 2025-04-11
Large reasoning models (LRMs) have recently shown impressive capabilities in complex reasoning by leveraging increased test-time computation and exhibiting behaviors akin to human-like deliberation. Despite these advances, it remains an open question whether LRMs are better calibrated - particularly ... more
-3.8817 AgentFM: Role-Aware Failure Management for Distributed Databases with LLM-Driven Multi-Agents
Authors: Lingzhe Zhang, Yunpeng Zhai, Tong Jia, Xiaosong Huang, Chiming Duan, Ying Li | Date: 2025-04-11
Distributed databases are critical infrastructures for today's large-scale software systems, making effective failure management essential to ensure software availability. However, existing approaches often overlook the role distinctions within distributed databases and rely on small-scale models wi ... more
-3.8859 Privacy Attacks on Image AutoRegressive Models
Authors: Antoni Kowalczuk, Jan Dubi\'nski, Franziska Boenisch, Adam Dziedzic | Date: 2025-04-11
Image autoregressive generation has emerged as a powerful new paradigm, with image autoregressive models (IARs) matching state-of-the-art diffusion models (DMs) in image quality (FID: 1.48 vs. 1.58) while allowing for higher generation speed. However, the privacy risks associated with IARs remain un ... more
-3.8868 Earth-Adapter: Bridge the Geospatial Domain Gaps with Mixture of Frequency Adaptation
Authors: Xiaoxing Hu, Ziyang Gong, Yupei Wang, Yuru Jia, Gen Luo, Xue Yang | Date: 2025-04-11
Parameter-Efficient Fine-Tuning (PEFT) is a technique that allows us to adapt powerful Foundation Models (FMs) to diverse downstream tasks while preserving and unleashing their inherent capabilities. However, we have observed that existing PEFT methods, which are often designed with natural imagery ... more
-3.8869 Discovering Influential Neuron Path in Vision Transformers
Authors: Yifan Wang, Yifei Liu, Yingdong Shi, Changming Li, Anqi Pang, Sibei Yang, Jingyi Yu, Kan Ren | Date: 2025-04-11
Vision Transformer models exhibit immense power yet remain opaque to human understanding, posing challenges and risks for practical applications. While prior research has attempted to demystify these models through input attribution and neuron role analysis, there's been a notable gap in considering ... more
-3.8874 On Mixed-Precision Iterative Methods and Analysis for Nearly Completely Decomposable Markov Processes
Authors: Vasileios Kalantzis, Mark S. Squillante, Chai Wah Wu | Date: 2025-04-11
In this paper we consider the problem of computing the stationary distribution of nearly completely decomposable Markov processes, a well-established area in the classical theory of Markov processes with broad applications in the design, modeling, analysis and optimization of computer systems. We de ... more
-3.891 Dissimilar Batch Decompositions of Random Datasets
Authors: Ghurumuruhan Ganesan | Date: 2025-04-11
For better learning, large datasets are often split into small batches and fed sequentially to the predictive model. In this paper, we study such batch decompositions from a probabilistic perspective. We assume that data points (possibly corrupted) are drawn independently from a given space and defi ... more
-3.8925 Towards Holistic Prompt Craft
Authors: Joseph Lindley, Roger Whitham | Date: 2025-04-11
We present an account of an ongoing practice-based Design Research programme that explores the interaction affordances of real-time AI image generators. Based on our experiences from three installations, we reflect on the design of PromptJ, a user interface built around the concept of a prompt mixer ... more
-3.9002 Sculpting Subspaces: Constrained Full Fine-Tuning in LLMs for Continual Learning
Authors: Nikhil Shivakumar Nayak, Krishnateja Killamsetty, Ligong Han, Abhishek Bhandwaldar, Prateek Chanda, Kai Xu, Hao Wang, Aldo Pareja, Oleg Silkin, Mustafa Eyceoz, Akash Srivastava | Date: 2025-04-11
Continual learning in large language models (LLMs) is prone to catastrophic forgetting, where adapting to new tasks significantly degrades performance on previously learned ones. Existing methods typically rely on low-rank, parameter-efficient updates that limit the model's expressivity and introduc ... more
-3.9031 Pretraining Language Models for Diachronic Linguistic Change Discovery
Authors: Elisabeth Fittschen, Sabrina Li, Tom Lippincott, Leshem Choshen, Craig Messner | Date: 2025-04-11
Large language models (LLMs) have shown potential as tools for scientific discovery. This has engendered growing interest in their use in humanistic disciplines, such as historical linguistics and literary studies. These fields often construct arguments on the basis of delineations like genre, or mo ... more
-3.9036 UniBERT: Adversarial Training for Language-Universal Representations
Authors: Andrei-Marius Avram, Marian Lupa\c{s}cu, Dumitru-Clementin Cercel, Ionu\c{t} Mironic\u{a}, \c{S}tefan Tr\u{a}u\c{s}an-Matu | Date: 2025-04-11
This paper presents UniBERT, a compact multilingual language model that leverages an innovative training framework integrating three components: masked language modeling, adversarial training, and knowledge distillation. Pre-trained on a meticulously curated Wikipedia corpus spanning 107 languages, ... more
-3.9083 LogiDynamics: Unraveling the Dynamics of Logical Inference in Large Language Model Reasoning
Authors: Tianshi Zheng, Jiayang Cheng, Chunyang Li, Haochen Shi, Zihao Wang, Jiaxin Bai, Yangqiu Song, Ginny Y. Wong, Simon See | Date: 2025-04-11
Modern large language models (LLMs) employ various forms of logical inference, both implicitly and explicitly, when addressing reasoning tasks. Understanding how to optimally leverage these inference paradigms is critical for advancing LLMs' reasoning capabilities. This paper adopts an exploratory a ... more
-3.9108 FACT: Multinomial Misalignment Classification for Point Cloud Registration
Authors: Ludvig Dill\'en, Per-Erik Forss\'en, Johan Edstedt | Date: 2025-04-11
We present FACT, a method for predicting alignment quality (i.e., registration error) of registered lidar point cloud pairs. This is useful e.g. for quality assurance of large, automatically registered 3D models. FACT extracts local features from a registered pair and processes them with a point tra ... more
-3.9118 Benchmarking Multimodal CoT Reward Model Stepwise by Visual Program
Authors: Minghe Gao, Xuqi Liu, Zhongqi Yue, Yang Wu, Shuang Chen, Juncheng Li, Siliang Tang, Fei Wu, Tat-Seng Chua, Yueting Zhuang | Date: 2025-04-11
Recent advancements in reward signal usage for Large Language Models (LLMs) are remarkable. However, significant challenges exist when transitioning reward signal to the multimodal domain, including labor-intensive annotations, over-reliance on one-step rewards, and inadequate evaluation. To address ... more
-3.9247 Are We Done with Object-Centric Learning?
Authors: Alexander Rubinstein, Ameya Prabhu, Matthias Bethge, Seong Joon Oh | Date: 2025-04-11
Object-centric learning (OCL) seeks to learn representations that only encode an object, isolated from other objects or background cues in a scene. This approach underpins various aims, including out-of-distribution (OOD) generalization, sample-efficient composition, and modeling of structured envir ... more
-3.9256 Similarity of Neural Network Models: A Survey of Functional and Representational Measures
Authors: Max Klabunde, Tobias Schumacher, Markus Strohmaier, Florian Lemmerich | Date: 2025-04-11
Measuring similarity of neural networks to understand and improve their behavior has become an issue of great importance and research interest. In this survey, we provide a comprehensive overview of two complementary perspectives of measuring neural network similarity: (i) representational similarit ... more
-3.9262 S'MoRE: Structural Mixture of Residual Experts for LLM Fine-tuning
Authors: Hanqing Zeng, Yinglong Xia, Zhuokai Zhao, Gilbert Jiang, Qiang Zhang, Jiayi Liu, Lizhu Zhang, Xiangjun Fan, Benyu Zhang | Date: 2025-04-11
Fine-tuning pre-trained large language models (LLMs) presents a dual challenge of balancing parameter efficiency and model capacity. Existing methods like low-rank adaptations (LoRA) are efficient but lack flexibility, while Mixture-of-Experts (MoE) architectures enhance model capacity at the cost o ... more
-3.938 FuseRL: Dense Preference Optimization for Heterogeneous Model Fusion
Authors: Longguang Zhong, Fanqi Wan, Ziyi Yang, Guosheng Liang, Tianyuan Shi, Xiaojun Quan | Date: 2025-04-11
Heterogeneous model fusion enhances the performance of LLMs by integrating the knowledge and capabilities of multiple structurally diverse models. However, existing approaches often rely solely on selecting the best output for each prompt from source models, which underutilizes their full potential ... more
-3.9398 ProHap Explorer: Visualizing Haplotypes in Proteogenomic Datasets
Authors: Jakub Va\v{s}\'i\v{c}ek, Dafni Skiadopoulou, Ksenia G. Kuznetsova, Lukas K\"all, Marc Vaudel, Stefan Bruckner | Date: 2025-04-11
In mass spectrometry-based proteomics, experts usually project data onto a single set of reference sequences, overlooking the influence of common haplotypes (combinations of genetic variants inherited together from a parent). We recently introduced ProHap, a tool for generating customized protein ha ... more
-3.943 Polygon: Symbolic Reasoning for SQL using Conflict-Driven Under-Approximation Search
Authors: Pinhan Zhao, Yuepeng Wang, Xinyu Wang | Date: 2025-04-11
We present a novel symbolic reasoning engine for SQL which can efficiently generate an input $I$ for $n$ queries $P_1, \cdots, P_n$, such that their outputs on $I$ satisfy a given property (expressed in SMT). This is useful in different contexts, such as disproving equivalence of two SQL queries and ... more
-3.945 Addressing Cold-start Problem in Click-Through Rate Prediction via Supervised Diffusion Modeling
Authors: Wenqiao Zhu, Lulu Wang, Jun Wu | Date: 2025-04-11
Predicting Click-Through Rates is a crucial function within recommendation and advertising platforms, as the output of CTR prediction determines the order of items shown to users. The Embedding \& MLP paradigm has become a standard approach for industrial recommendation systems and has been widely d ... more
-3.9481 CroissantLLM: A Truly Bilingual French-English Language Model
Authors: Manuel Faysse, Patrick Fernandes, Nuno M. Guerreiro, Ant\'onio Loison, Duarte M. Alves, Caio Corro, Nicolas Boizard, Jo\~ao Alves, Ricardo Rei, Pedro H. Martins, Antoni Bigata Casademunt, Fran\c{c}ois Yvon, Andr\'e F. T. Martins, Gautier Viaud, C\'eline Hudelot, Pierre Colombo | Date: 2025-04-11
We introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware. To that end, we pioneer the approach of t ... more
-3.9505 Preference-Based Alignment of Discrete Diffusion Models
Authors: Umberto Borso, Davide Paglieri, Jude Wells, Tim Rockt\"aschel | Date: 2025-04-11
Diffusion models have achieved state-of-the-art performance across multiple domains, with recent advancements extending their applicability to discrete data. However, aligning discrete diffusion models with task-specific preferences remains challenging, particularly in scenarios where explicit rewar ... more
-3.9531 TabKAN: Advancing Tabular Data Analysis using Kolmograv-Arnold Network
Authors: Ali Eslamian, Alireza Afzal Aghaei, Qiang Cheng | Date: 2025-04-11
Tabular data analysis presents unique challenges due to its heterogeneous feature types, missing values, and complex interactions. While traditional machine learning methods, such as gradient boosting, often outperform deep learning approaches, recent advancements in neural architectures offer promi ... more
-3.9584 Disentangle and Regularize: Sign Language Production with Articulator-Based Disentanglement and Channel-Aware Regularization
Authors: Sumeyye Meryem Tasyurek, Tugce Kiziltepe, Hacer Yalim Keles | Date: 2025-04-11
In this work, we propose a simple gloss-free, transformer-based sign language production (SLP) framework that directly maps spoken-language text to sign pose sequences. We first train a pose autoencoder that encodes sign poses into a compact latent space using an articulator-based disentanglement st ... more
-3.9633 Large Language Model Can Be a Foundation for Hidden Rationale-Based Retrieval
Authors: Luo Ji, Feixiang Guo, Teng Chen, Qingqing Gu, Xiaoyu Wang, Ningyuan Xi, Yihong Wang, Peng Yu, Yue Zhao, Hongyang Lei, Zhonglin Jiang, Yong Chen | Date: 2025-04-11
Despite the recent advancement in Retrieval-Augmented Generation (RAG) systems, most retrieval methodologies are often developed for factual retrieval, which assumes query and positive documents are semantically similar. In this paper, we instead propose and study a more challenging type of retrieva ... more
-3.9651 SEAL: Semantic Aware Image Watermarking
Authors: Kasra Arabi, R. Teal Witter, Chinmay Hegde, Niv Cohen | Date: 2025-04-11
Generative models have rapidly evolved to generate realistic outputs. However, their synthetic outputs increasingly challenge the clear distinction between natural and AI-generated content, necessitating robust watermarking techniques. Watermarks are typically expected to preserve the integrity of t ... more
-3.9664 EzSQL: An SQL intermediate representation for improving SQL-to-text Generation
Authors: Meher Bhardwaj, Hrishikesh Ethari, Dennis Singh Moirangthem | Date: 2025-04-11
The SQL-to-text generation task traditionally uses template base, Seq2Seq, tree-to-sequence, and graph-to-sequence models. Recent models take advantage of pre-trained generative language models for this task in the Seq2Seq framework. However, treating SQL as a sequence of inputs to the pre-trained m ... more
-3.9665 Atlas Gaussians Diffusion for 3D Generation
Authors: Haitao Yang, Yuan Dong, Hanwen Jiang, Dejia Xu, Georgios Pavlakos, Qixing Huang | Date: 2025-04-11
Using the latent diffusion model has proven effective in developing novel 3D generation techniques. To harness the latent diffusion model, a key challenge is designing a high-fidelity and efficient representation that links the latent space and the 3D space. In this paper, we introduce Atlas Gaussia ... more
-3.9677 Automatically Generating Single-Responsibility Unit Tests
Authors: Geraldine Galindo-Gutierrez | Date: 2025-04-11
Automatic test generation aims to save developers time and effort by producing test suites with reasonably high coverage and fault detection. However, the focus of search-based generation tools in maximizing coverage leaves other properties, such as test quality, coincidental. The evidence shows tha ... more
-3.9795 An Analysis of Temporal Dropout in Earth Observation Time Series for Regression Tasks
Authors: Miro Miranda, Francisco Mena, Andreas Dengel | Date: 2025-04-11
Missing instances in time series data impose a significant challenge to deep learning models, particularly in regression tasks. In the Earth Observation field, satellite failure or cloud occlusion frequently results in missing time-steps, introducing uncertainties in the predicted output and causing ... more
-3.9915 Right Prediction, Wrong Reasoning: Uncovering LLM Misalignment in RA Disease Diagnosis
Authors: Umakanta Maharana, Sarthak Verma, Avarna Agarwal, Prakashini Mruthyunjaya, Dwarikanath Mahapatra, Sakir Ahmed, Murari Mandal | Date: 2025-04-11
Large language models (LLMs) offer a promising pre-screening tool, improving early disease detection and providing enhanced healthcare access for underprivileged communities. The early diagnosis of various diseases continues to be a significant challenge in healthcare, primarily due to the nonspecif ... more
-3.9952 Symbolic Parallel Composition for Multi-language Protocol Verification
Authors: Faezeh Nasrabadi, Robert K\"unnemann, Hamed Nemati | Date: 2025-04-11
The implementation of security protocols often combines different languages. This practice, however, poses a challenge to traditional verification techniques, which typically assume a single-language environment and, therefore, are insufficient to handle challenges presented by the interplay of diff ... more
-3.9967 Probability Density Geodesics in Image Diffusion Latent Space
Authors: Qingtao Yu, Jaskirat Singh, Zhaoyuan Yang, Peter Henry Tu, Jing Zhang, Hongdong Li, Richard Hartley, Dylan Campbell | Date: 2025-04-11
Diffusion models indirectly estimate the probability density over a data space, which can be used to study its structure. In this work, we show that geodesics can be computed in diffusion latent space, where the norm induced by the spatially-varying inner product is inversely proportional to the pro ... more
-3.9973 Latent Diffusion U-Net Representations Contain Positional Embeddings and Anomalies
Authors: Jonas Loos, Lorenz Linhardt | Date: 2025-04-11
Diffusion models have demonstrated remarkable capabilities in synthesizing realistic images, spurring interest in using their representations for various downstream tasks. To better understand the robustness of these representations, we analyze popular Stable Diffusion models using representational ... more
-3.9978 A Metropolis-Adjusted Langevin Algorithm for Sampling Jeffreys Prior
Authors: Yibo Shi, Braghadeesh Lakshminarayanan, Cristian R. Rojas | Date: 2025-04-11
Inference and estimation are fundamental aspects of statistics, system identification and machine learning. For most inference problems, prior knowledge is available on the system to be modeled, and Bayesian analysis is a natural framework to impose such prior information in the form of a prior dist ... more
-4.0061 Don't Let It Hallucinate: Premise Verification via Retrieval-Augmented Logical Reasoning
Authors: Yuehan Qin, Shawn Li, Yi Nian, Xinyan Velocity Yu, Yue Zhao, Xuezhe Ma | Date: 2025-04-11
Large language models (LLMs) have shown substantial capacity for generating fluent, contextually appropriate responses. However, they can produce hallucinated outputs, especially when a user query includes one or more false premises-claims that contradict established facts. Such premises can mislead ... more
-4.0256 Evaluating Retrieval Augmented Generative Models for Document Queries in Transportation Safety
Authors: Chad Melton, Alex Sorokine, Steve Peterson | Date: 2025-04-11
Applications of generative Large Language Models LLMs are rapidly expanding across various domains, promising significant improvements in workflow efficiency and information retrieval. However, their implementation in specialized, high-stakes domains such as hazardous materials transportation is cha ... more
-4.0263 Noise-based Local Learning using Stochastic Magnetic Tunnel Junctions
Authors: Kees Koenders, Leo Schnitzpan, Fabian Kammerbauer, Sinan Shu, Gerhard Jakob, Mathis Kl\"aui, Johan Mentink, Nasir Ahmad, Marcel van Gerven | Date: 2025-04-11
Brain-inspired learning in physical hardware has enormous potential to learn fast at minimal energy expenditure. One of the characteristics of biological learning systems is their ability to learn in the presence of various noise sources. Inspired by this observation, we introduce a novel noise-base ... more
-4.03 Kaleidoscope: In-language Exams for Massively Multilingual Vision Evaluation
Authors: Israfel Salazar, Manuel Fern\'andez Burda, Shayekh Bin Islam, Arshia Soltani Moakhar, Shivalika Singh, Fabian Farestam, Angelika Romanou, Danylo Boiko, Dipika Khullar, Mike Zhang, Dominik Krzemi\'nski, Jekaterina Novikova, Lu\'isa Shimabucoro, Joseph Marvin Imperial, Rishabh Maheshwary, Sharad Duwal, Alfonso Amayuelas, Swati Rajwal, Jebish Purbey, Ahmed Ruby, Nicholas Popovi\v{c}, Marek Suppa, Azmine Toushik Wasi, Ram Mohan Rao Kadiyala, Olga Tsymboi, Maksim Kostritsya, Bardia Soltani Moakhar, Gabriel da Costa Merlin, Ot\'avio Ferracioli Coletti, Maral Jabbari Shiviari, MohammadAmin farahani fard, Silvia Fernandez, Mar\'ia Grandury, Dmitry Abulkhanov, Drishti Sharma, Andre Guarnier De Mitri, Leticia Bossatto Marchezi, Johan Obando-Ceron, Nazar Kohut, Beyza Ermis, Desmond Elliott, Enzo Ferrante, Sara Hooker, Marzieh Fadaee | Date: 2025-04-11
The evaluation of vision-language models (VLMs) has mainly relied on English-language benchmarks, leaving significant gaps in both multilingual and multicultural coverage. While multilingual benchmarks have expanded, both in size and languages, many rely on translations of English datasets, failing ... more
-4.0303 UMGAD: Unsupervised Multiplex Graph Anomaly Detection
Authors: Xiang Li, Jianpeng Qi, Zhongying Zhao, Guanjie Zheng, Lei Cao, Junyu Dong, Yanwei Yu | Date: 2025-04-11
Graph anomaly detection (GAD) is a critical task in graph machine learning, with the primary objective of identifying anomalous nodes that deviate significantly from the majority. This task is widely applied in various real-world scenarios, including fraud detection and social network analysis. Howe ... more
-4.0343 Using Large Language Models to Develop Requirements Elicitation Skills
Authors: Nelson Lojo (Univ. of California, Berkeley, CA, USA), Rafael Gonz\'alez (SCORE Lab, Univ. of Sevilla, Sevilla, Spain), Rohan Philip (Oak Park High School, Oak Park, USA), Jos\'e Antonio Parejo (SCORE Lab, Univ. of Sevilla, Sevilla, Spain), Amador Dur\'an Toro (SCORE Lab, Univ. of Sevilla, Sevilla, Spain), Armando Fox (Univ. of California, Berkeley, CA, USA), Pablo Fern\'andez (SCORE Lab, Univ. of Sevilla, Sevilla, Spain) | Date: 2025-04-11
Requirements Elicitation (RE) is a crucial software engineering skill that involves interviewing a client and then devising a software design based on the interview results. Teaching this inherently experiential skill effectively has high cost, such as acquiring an industry partner to interview, or ... more
-4.0379 FedSECA: Sign Election and Coordinate-wise Aggregation of Gradients for Byzantine Tolerant Federated Learning
Authors: Joseph Geo Benjamin, Mothilal Asokan, Mohammad Yaqub, Karthik Nandakumar | Date: 2025-04-11
One of the most common defense strategies against Byzantine clients in federated learning (FL) is to employ a robust aggregator mechanism that makes the training more resilient. While many existing Byzantine robust aggregators provide theoretical convergence guarantees and are empirically effective ... more
-4.0415 FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion
Authors: Xing Han, Huy Nguyen, Carl Harris, Nhat Ho, Suchi Saria | Date: 2025-04-11
As machine learning models in critical fields increasingly grapple with multimodal data, they face the dual challenges of handling a wide array of modalities, often incomplete due to missing elements, and the temporal irregularity and sparsity of collected samples. Successfully leveraging this compl ... more
-4.042 Balancing Rigor and Utility: Mitigating Cognitive Biases in Large Language Models for Multiple-Choice Questions
Authors: Hanyang Zhong, Liman Wang, Wenting Cao, Zeyuan Sun | Date: 2025-04-11
This paper examines the role of cognitive biases in the decision-making processes of large language models (LLMs), challenging the conventional goal of eliminating all biases. When properly balanced, we show that certain cognitive biases can enhance decision-making efficiency through rational deviat ... more
-4.0444 Intent Representation Learning with Large Language Model for Recommendation
Authors: Yu Wang, Lei Sang, Yi Zhang, Yiwen Zhang | Date: 2025-04-11
Intent-based recommender systems have garnered significant attention for uncovering latent fine-grained preferences. Intents, as underlying factors of interactions, are crucial for improving recommendation interpretability. Most methods define intents as learnable parameters updated alongside intera ... more
-4.0447 Non-Normalized Solutions of Generalized Nash Equilibrium in Autonomous Racing
Authors: Mark Pustilnik, Antonio Loquercio, Francesco Borrelli | Date: 2025-04-11
In dynamic games with shared constraints, Generalized Nash Equilibria (GNE) are often computed using the normalized solution concept, which assumes identical Lagrange multipliers for shared constraints across all players. While widely used, this approach excludes other potentially valuable GNE. This ... more
-4.0485 Dynamic Evaluation Framework for Personalized and Trustworthy Agents: A Multi-Session Approach to Preference Adaptability
Authors: Chirag Shah, Hideo Joho, Kirandeep Kaur, Preetam Prabhu Srikar Dammu | Date: 2025-04-11
Recent advancements in generative AI have significantly increased interest in personalized agents. With increased personalization, there is also a greater need for being able to trust decision-making and action taking capabilities of these agents. However, the evaluation methods for these agents rem ... more
-4.0626 To Backtrack or Not to Backtrack: When Sequential Search Limits Model Reasoning
Authors: Tian Qin, David Alvarez-Melis, Samy Jelassi, Eran Malach | Date: 2025-04-11
Recent advancements in large language models have significantly improved their reasoning abilities, particularly through techniques involving search and backtracking. Backtracking naturally scales test-time compute by enabling sequential, linearized exploration via long chain-of-thought (CoT) genera ... more
-4.0631 Prompting or Fine-tuning? Exploring Large Language Models for Causal Graph Validation
Authors: Yuni Susanti, Nina Holsmoelle | Date: 2025-04-11
This study explores the capability of Large Language Models (LLMs) to evaluate causality in causal graphs generated by conventional statistical causal discovery methods-a task traditionally reliant on manual assessment by human subject matter experts. To bridge this gap in causality assessment, LLMs ... more
-4.0677 Bottom-Up Generation of Verilog Designs for Testing EDA Tools
Authors: Jo\~ao Victor Amorim Vieira, Luiza de Melo Gomes, Rafael Sumitani, Raissa Maciel, Augusto Mafra, Mirlaine Crepalde, Fernando Magno Quint\~ao Pereira | Date: 2025-04-11
Testing Electronic Design Automation (EDA) tools rely on benchmarks -- designs written in Hardware Description Languages (HDLs) such as Verilog, SystemVerilog, or VHDL. Although collections of benchmarks for these languages exist, they are typically limited in size. This scarcity has recently drawn ... more
Hardware
-4.0677 CAFE-AD: Cross-Scenario Adaptive Feature Enhancement for Trajectory Planning in Autonomous Driving
Authors: Junrui Zhang, Chenjie Wang, Jie Peng, Haoyu Li, Jianmin Ji, Yu Zhang, Yanyong Zhang | Date: 2025-04-11
Imitation learning based planning tasks on the nuPlan dataset have gained great interest due to their potential to generate human-like driving behaviors. However, open-loop training on the nuPlan dataset tends to cause causal confusion during closed-loop testing, and the dataset also presents a long ... more
-4.074 KG-LLM-Bench: A Scalable Benchmark for Evaluating LLM Reasoning on Textualized Knowledge Graphs
Authors: Elan Markowitz, Krupa Galiya, Greg Ver Steeg, Aram Galstyan | Date: 2025-04-11
Knowledge graphs have emerged as a popular method for injecting up-to-date, factual knowledge into large language models (LLMs). This is typically achieved by converting the knowledge graph into text that the LLM can process in context. While multiple methods of encoding knowledge graphs have been p ... more
-4.0789 ColorizeDiffusion v2: Enhancing Reference-based Sketch Colorization Through Separating Utilities
Authors: Dingkun Yan, Xinrui Wang, Yusuke Iwasawa, Yutaka Matsuo, Suguru Saito, Jiaxian Guo | Date: 2025-04-11
Reference-based sketch colorization methods have garnered significant attention due to their potential applications in the animation production industry. However, most existing methods are trained with image triplets of sketch, reference, and ground truth that are semantically and spatially well-ali ... more
-4.079 HGFormer: Topology-Aware Vision Transformer with HyperGraph Learning
Authors: Hao Wang, Shuo Zhang, Biao Leng | Date: 2025-04-11
The computer vision community has witnessed an extensive exploration of vision transformers in the past two years. Drawing inspiration from traditional schemes, numerous works focus on introducing vision-specific inductive biases. However, the implicit modeling of permutation invariance and fully-co ... more
-4.0797 OPAL: Encoding Causal Understanding of Physical Systems for Robot Learning
Authors: Daniel Tcheurekdjian, Joshua Klasmeier, Tom Cooney, Christopher McCann, Tyler Fenstermaker | Date: 2025-04-11
We present OPAL (Operant Physical Agent with Language), a novel vision-language-action architecture that introduces topological constraints to flow matching for robotic control. To do so, we further introduce topological attention. Our approach models action sequences as topologically-structured rep ... more
-4.0802 OmniCaptioner: One Captioner to Rule Them All
Authors: Yiting Lu, Jiakang Yuan, Zhen Li, Shitian Zhao, Qi Qin, Xinyue Li, Le Zhuo, Licheng Wen, Dongyang Liu, Yuewen Cao, Xiangchao Yan, Xin Li, Botian Shi, Tao Chen, Zhibo Chen, Lei Bai, Bo Zhang, Peng Gao | Date: 2025-04-11
We propose OmniCaptioner, a versatile visual captioning framework for generating fine-grained textual descriptions across a wide variety of visual domains. Unlike prior methods limited to specific image types (e.g., natural images or geometric visuals), our framework provides a unified solution for ... more
-4.0804 Network inference via approximate Bayesian computation. Illustration on a stochastic multi-population neural mass model
Authors: Susanne Ditlevsen, Massimiliano Tamborrino, Irene Tubikanec | Date: 2025-04-11
In this article, we propose an adapted sequential Monte Carlo approximate Bayesian computation (SMC-ABC) algorithm for network inference in coupled stochastic differential equations (SDEs) used for multivariate time series modeling. Our approach is motivated by neuroscience, specifically the challen ... more
-4.0854 Sparsified-Learning for Heavy-Tailed Locally Stationary Processes
Authors: Yingjie Wang, Mokhtar Z. Alaya, Salim Bouzebda, Xinsheng Liu | Date: 2025-04-11
Sparsified Learning is ubiquitous in many machine learning tasks. It aims to regularize the objective function by adding a penalization term that considers the constraints made on the learned parameters. This paper considers the problem of learning heavy-tailed LSP. We develop a flexible and robust ... more
-4.0862 Distilling Textual Priors from LLM to Efficient Image Fusion
Authors: Ran Zhang, Xuanhua He, Ke Cao, Liu Liu, Li Zhang, Man Zhou, Jie Zhang | Date: 2025-04-11
Multi-modality image fusion aims to synthesize a single, comprehensive image from multiple source inputs. Traditional approaches, such as CNNs and GANs, offer efficiency but struggle to handle low-quality or complex inputs. Recent advances in text-guided methods leverage large model priors to overco ... more
-4.0866 PARDON: Privacy-Aware and Robust Federated Domain Generalization
Authors: Dung Thuy Nguyen, Taylor T. Johnson, Kevin Leach | Date: 2025-04-11
Federated Learning (FL) shows promise in preserving privacy and enabling collaborative learning. However, most current solutions focus on private data collected from a single domain. A significant challenge arises when client data comes from diverse domains (i.e., domain shift), leading to poor perf ... more
-4.0931 StealthRank: LLM Ranking Manipulation via Stealthy Prompt Optimization
Authors: Yiming Tang, Yi Fan, Chenxiao Yu, Tiankai Yang, Yue Zhao, Xiyang Hu | Date: 2025-04-11
The integration of large language models (LLMs) into information retrieval systems introduces new attack surfaces, particularly for adversarial ranking manipulations. We present StealthRank, a novel adversarial ranking attack that manipulates LLM-driven product recommendation systems while maintaini ... more
-4.0951 CRYSIM: Prediction of Symmetric Structures of Large Crystals with GPU-based Ising Machines
Authors: Chen Liang, Diptesh Das, Jiang Guo, Ryo Tamura, Zetian Mao, Koji Tsuda | Date: 2025-04-11
Solving black-box optimization problems with Ising machines is increasingly common in materials science. However, their application to crystal structure prediction (CSP) is still ineffective due to symmetry agnostic encoding of atomic coordinates. We introduce CRYSIM, an algorithm that encodes the s ... more
-4.0976 Multi-objective Optimization in CPU Design Space Exploration: Attention is All You Need
Authors: Runzhen Xue, Hao Wu, Mingyu Yan, Ziheng Xiao, Xiaochun Ye, Dongrui Fan | Date: 2025-04-11
Design space exploration (DSE) enables architects to systematically evaluate various design options, guiding decisions on the most suitable configurations to meet specific objectives such as optimizing performance, power, and area. However, the growing complexity of modern CPUs has dramatically incr ... more
Hardware
-4.0985 UKBOB: One Billion MRI Labeled Masks for Generalizable 3D Medical Image Segmentation
Authors: Emmanuelle Bourigault, Amir Jamaludin, Abdullah Hamdi | Date: 2025-04-11
In medical imaging, the primary challenge is collecting large-scale labeled data due to privacy concerns, logistics, and high labeling costs. In this work, we present the UK Biobank Organs and Bones (UKBOB), the largest labeled dataset of body organs, comprising 51,761 MRI 3D samples (equivalent to ... more
-4.1023 ClarityEthic: Explainable Moral Judgment Utilizing Contrastive Ethical Insights from Large Language Models
Authors: Yuxi Sun, Wei Gao, Jing Ma, Hongzhan Lin, Ziyang Luo, Wenxuan Zhang | Date: 2025-04-11
With the rise and widespread use of Large Language Models (LLMs), ensuring their safety is crucial to prevent harm to humans and promote ethical behaviors. However, directly assessing value valence (i.e., support or oppose) by leveraging large-scale data training is untrustworthy and inexplainable. ... more
-4.1051 Leveraging AI-Generated Emotional Self-Voice to Nudge People towards their Ideal Selves
Authors: Cathy Mengying Fang, Phoebe Chua, Samantha Chan, Joanne Leong, Andria Bao, Pattie Maes | Date: 2025-04-11
Emotions, shaped by past experiences, significantly influence decision-making and goal pursuit. Traditional cognitive-behavioral techniques for personal development rely on mental imagery to envision ideal selves, but may be less effective for individuals who struggle with visualization. This paper ... more
-4.1071 Efficient Storage Integrity in Adversarial Settings
Authors: Quinn Burke, Ryan Sheatsley, Yohan Beugin, Eric Pauley, Owen Hines, Michael Swift, Patrick McDaniel | Date: 2025-04-11
Storage integrity is essential to systems and applications that use untrusted storage (e.g., public clouds, end-user devices). However, known methods for achieving storage integrity either suffer from high (and often prohibitive) overheads or provide weak integrity guarantees. In this work, we demon ... more
-4.1133 Orchestrate Multimodal Data with Batch Post-Balancing to Accelerate Multimodal Large Language Model Training
Authors: Yijie Zheng, Bangjun Xiao, Lei Shi, Xiaoyang Li, Faming Wu, Tianyu Li, Xuefeng Xiao, Yang Zhang, Yuxuan Wang, Shouda Liu | Date: 2025-04-11
Multimodal large language models (MLLMs), such as GPT-4o, are garnering significant attention. During the exploration of MLLM training, we identified Modality Composition Incoherence, a phenomenon that the proportion of a certain modality varies dramatically across different examples. It exacerbates ... more
-4.1143 CHIME: A Compressive Framework for Holistic Interest Modeling
Authors: Yong Bai, Rui Xiang, Kaiyuan Li, Yongxiang Tang, Yanhua Cheng, Xialong Liu, Peng Jiang, Kun Gai | Date: 2025-04-11
Modeling holistic user interests is important for improving recommendation systems but is challenged by high computational cost and difficulty in handling diverse information with full behavior context. Existing search-based methods might lose critical signals during behavior selection. To overcome ... more
-4.1167 MoC-System: Efficient Fault Tolerance for Sparse Mixture-of-Experts Model Training
Authors: Weilin Cai, Le Qin, Jiayi Huang | Date: 2025-04-11
As large language models continue to scale up, distributed training systems have expanded beyond 10k nodes, intensifying the importance of fault tolerance. Checkpoint has emerged as the predominant fault tolerance strategy, with extensive studies dedicated to optimizing its efficiency. However, the ... more
-4.1235 User-Centered AI for Data Exploration: Rethinking GenAI's Role in Visualization
Authors: Kathrin Schnizer, Sven Mayer | Date: 2025-04-11
Recent advances in GenAI have enabled automation in data visualization, allowing users to generate visual representations using natural language. However, existing systems primarily focus on automation, overlooking users' varying expertise levels and analytical needs. In this position paper, we advo ... more
-4.1259 Unifying Autoregressive and Diffusion-Based Sequence Generation
Authors: Nima Fathi, Torsten Scholak, Pierre-Andr\'e No\"el | Date: 2025-04-11
We present significant extensions to diffusion-based sequence generation models, blurring the line with autoregressive language models. We introduce hyperschedules, which assign distinct noise schedules to individual token positions, generalizing both autoregressive models (e.g., GPT) and convention ... more
-4.1259 Navigating Explanatory Multiverse Through Counterfactual Path Geometry
Authors: Kacper Sokol, Edward Small, Yueqing Xuan | Date: 2025-04-11
Counterfactual explanations are the de facto standard when tasked with interpreting decisions of (opaque) predictive models. Their generation is often subject to technical and domain-specific constraints that aim to maximise their real-life utility. In addition to considering desiderata pertaining t ... more
-4.1268 Data-driven Power Loss Identification through Physics-Based Thermal Model Backpropagation
Authors: Mattia Scarpa, Francesco Pase, Ruggero Carli, Mattia Bruschetta, Franscesco Toso | Date: 2025-04-11
Digital twins for power electronics require accurate power losses whose direct measurements are often impractical or impossible in real-world applications. This paper presents a novel hybrid framework that combines physics-based thermal modeling with data-driven techniques to identify and correct po ... more
-4.1277 PingPong: A Benchmark for Role-Playing Language Models with User Emulation and Multi-Model Evaluation
Authors: Ilya Gusev | Date: 2025-04-11
We introduce a benchmark for evaluating the role-playing capabilities of language models. Our approach leverages different language models to simulate users in dynamic, multi-turn conversations and assess the resulting dialogues. Our methodology involves three main components: a player model that ad ... more
LLMs
-4.1289 Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models
Authors: Qiguang Chen, Libo Qin, Jinhao Liu, Dengyun Peng, Jiannan Guan, Peng Wang, Mengkang Hu, Yuhang Zhou, Te Gao, Wanxiang Che | Date: 2025-04-11
Recent advancements in reasoning with large language models (RLLMs), such as OpenAI-O1 and DeepSeek-R1, have demonstrated their impressive capabilities in complex domains like mathematics and coding. A central factor in their success lies in the application of long chain-of-thought (Long CoT) charac ... more
-4.1399 MonoPlace3D: Learning 3D-Aware Object Placement for 3D Monocular Detection
Authors: Rishubh Parihar, Srinjay Sarkar, Sarthak Vora, Jogendra Kundu, R. Venkatesh Babu | Date: 2025-04-11
Current monocular 3D detectors are held back by the limited diversity and scale of real-world datasets. While data augmentation certainly helps, it's particularly difficult to generate realistic scene-aware augmented data for outdoor settings. Most current approaches to synthetic data generation foc ... more
-4.1438 Self-Steering Language Models
Authors: Gabriel Grand, Joshua B. Tenenbaum, Vikash K. Mansinghka, Alexander K. Lew, Jacob Andreas | Date: 2025-04-11
While test-time reasoning enables language models to tackle complex tasks, searching or planning in natural language can be slow, costly, and error-prone. But even when LMs struggle to emulate the precise reasoning steps needed to solve a problem, they often excel at describing its abstract structur ... more
-4.1534 From Broadcast to Minimap: Achieving State-of-the-Art SoccerNet Game State Reconstruction
Authors: Vladimir Golovkin, Nikolay Nemtsev, Vasyl Shandyba, Oleg Udin, Nikita Kasatkin, Pavel Kononov, Anton Afanasiev, Sergey Ulasen, Andrei Boiarov | Date: 2025-04-11
Game State Reconstruction (GSR), a critical task in Sports Video Understanding, involves precise tracking and localization of all individuals on the football field-players, goalkeepers, referees, and others - in real-world coordinates. This capability enables coaches and analysts to derive actionabl ... more
-4.1546 A Concise Mathematical Description of Active Inference in Discrete Time
Authors: Jesse van Oostrum, Carlotta Langer, Nihat Ay | Date: 2025-04-11
In this paper we present a concise mathematical description of active inference in discrete time. The main part of the paper serves as a basic introduction to the topic, including a detailed example of the action selection mechanism. The appendix discusses the more subtle mathematical details, targe ... more
-4.1579 Leanabell-Prover: Posttraining Scaling in Formal Reasoning
Authors: Jingyuan Zhang, Qi Wang, Xingguang Ji, Yahui Liu, Yang Yue, Fuzheng Zhang, Di Zhang, Guorui Zhou, Kun Gai | Date: 2025-04-11
Recent advances in automated theorem proving (ATP) through LLMs have highlighted the potential of formal reasoning with Lean 4 codes. However, ATP has not yet be revolutionized by the recent posttraining scaling as demonstrated by Open AI O1/O3 and Deepseek R1. In this work, we investigate the entir ... more
-4.1586 Discrete-to-continuum limit for nonlinear reaction-diffusion systems via EDP convergence for gradient systems
Authors: Georg Heinze, Alexander Mielke, Artur Stephan | Date: 2025-04-11
We investigate the convergence of spatial discretizations for reaction-diffusion systems with mass-action law satisfying a detailed balance condition. Considering systems on the d-dimensional torus, we construct appropriate space-discrete processes and show convergence not only on the level of solut ... more
-4.1594 Locally Stationary Distributions: A Framework for Analyzing Slow-Mixing Markov Chains
Authors: Kuikui Liu, Sidhanth Mohanty, Prasad Raghavendra, Amit Rajaraman, David X. Wu | Date: 2025-04-11
Many natural Markov chains fail to mix to their stationary distribution in polynomially many steps. Often, this slow mixing is inevitable since it is computationally intractable to sample from their stationary measure.
-4.1616 Synthetic News Generation for Fake News Classification
Authors: Abdul Sittar, Luka Golob, Mateja Smiljanic | Date: 2025-04-11
This study explores the generation and evaluation of synthetic fake news through fact based manipulations using large language models (LLMs). We introduce a novel methodology that extracts key facts from real articles, modifies them, and regenerates content to simulate fake news while maintaining co ... more
-4.1648 Randomness, Not Representation: The Unreliability of Evaluating Cultural Alignment in LLMs
Authors: Ariba Khan, Stephen Casper, Dylan Hadfield-Menell | Date: 2025-04-11
Research on the 'cultural alignment' of Large Language Models (LLMs) has emerged in response to growing interest in understanding representation across diverse stakeholders. Current approaches to evaluating cultural alignment through survey-based assessments that borrow from social science methodolo ... more
-4.1659 The Power of the Pareto Front: Balancing Uncertain Rewards for Adaptive Experimentation in scanning probe microscopy
Authors: Yu Liu, Sergei V. Kalinin | Date: 2025-04-11
Automated experimentation has the potential to revolutionize scientific discovery, but its effectiveness depends on well-defined optimization targets, which are often uncertain or probabilistic in real-world settings. In this work, we demonstrate the application of Multi-Objective Bayesian Optimizat ... more
-4.1691 Locally Repairable Convertible Codes: Improved Lower Bound and General Construction
Authors: Songping Ge, Han Cai, Xiaohu Tang | Date: 2025-04-11
In this paper, we consider the convertible code with locally repairable property. We present an improved lower bound on access cost associated with $(r,\delta)$. Then, we provide a general construction of convertible codes with optimal access cost which shows that those codes can be with super-linea ... more
-4.1705 Higher-Order Color Voronoi Diagrams and the Colorful Clarkson-Shor Framework
Authors: Sang Won Bae, Nicolau Oliver, Evanthia Papadopoulou | Date: 2025-04-11
Given a set $S$ of $n$ colored sites, each $s\in S$ associated with a distance-to-site function $\delta_s \colon \mathbb{R}^2 \to \mathbb{R}$, we consider two distance-to-color functions for each color: one takes the minimum of $\delta_s$ for sites $s\in S$ in that color and the other takes the maxi ... more
-4.1708 AI, Help Me Think$\unicode{x2014}$but for Myself: Assisting People in Complex Decision-Making by Providing Different Kinds of Cognitive Support
Authors: Leon Reicherts, Zelun Tony Zhang, Elisabeth von Oswald, Yuanting Liu, Yvonne Rogers, Mariam Hassib | Date: 2025-04-11
How can we design AI tools that effectively support human decision-making by complementing and enhancing users' reasoning processes? Common recommendation-centric approaches face challenges such as inappropriate reliance or a lack of integration with users' decision-making processes. Here, we explor ... more
-4.175 FlashDepth: Real-time Streaming Video Depth Estimation at 2K Resolution
Authors: Gene Chou, Wenqi Xian, Guandao Yang, Mohamed Abdelfattah, Bharath Hariharan, Noah Snavely, Ning Yu, Paul Debevec | Date: 2025-04-11
A versatile video depth estimation model should (1) be accurate and consistent across frames, (2) produce high-resolution depth maps, and (3) support real-time streaming. We propose FlashDepth, a method that satisfies all three requirements, performing depth estimation on a 2044x1148 streaming video ... more
-4.1793 A Survey on Error-Bounded Lossy Compression for Scientific Datasets
Authors: Sheng Di, Jinyang Liu, Kai Zhao, Xin Liang, Robert Underwood, Zhaorui Zhang, Milan Shah, Yafan Huang, Jiajun Huang, Xiaodong Yu, Congrong Ren, Hanqi Guo, Grant Wilkins, Dingwen Tao, Jiannan Tian, Sian Jin, Zizhe Jian, Daoce Wang, MD Hasanur Rahman, Boyuan Zhang, Shihui Song, Jon C. Calhoun, Guanpeng Li, Kazutomo Yoshii, Khalid Ayed Alharthi, Franck Cappello | Date: 2025-04-11
Error-bounded lossy compression has been effective in significantly reducing the data storage/transfer burden while preserving the reconstructed data fidelity very well. Many error-bounded lossy compressors have been developed for a wide range of parallel and distributed use cases for years. They ar ... more
-4.181 RAVEN: An Agentic Framework for Multimodal Entity Discovery from Large-Scale Video Collections
Authors: Kevin Dela Rosa | Date: 2025-04-11
We present RAVEN an adaptive AI agent framework designed for multimodal entity discovery and retrieval in large-scale video collections. Synthesizing information across visual, audio, and textual modalities, RAVEN autonomously processes video data to produce structured, actionable representations fo ... more
-4.1877 Accelerating Hybrid XOR$-$CNF SAT Problems Natively with In-Memory Computing
Authors: Haesol Im, Fabian B\"ohm, Giacomo Pedretti, Noriyuki Kushida, Moslem Noori, Elisabetta Valiante, Xiangyi Zhang, Chan-Woo Yang, Tinish Bhattacharya, Xia Sheng, Jim Ignowski, Arne Heittmann, John Paul Strachan, Masoud Mohseni, Ray Beausoleil, Thomas Van Vaerenbergh, Ignacio Rozada | Date: 2025-04-11
The Boolean satisfiability (SAT) problem is a computationally challenging decision problem central to many industrial applications. For SAT problems in cryptanalysis, circuit design, and telecommunication, solutions can often be found more efficiently by representing them with a combination of exclu ... more
-4.1882 Generative AI Voting: Fair Collective Choice is Resilient to LLM Biases and Inconsistencies
Authors: Srijoni Majumdar, Edith Elkind, Evangelos Pournaras | Date: 2025-04-11
Scaling up deliberative and voting participation is a longstanding endeavor -- a cornerstone for direct democracy and legitimate collective choice. Recent breakthroughs in generative artificial intelligence (AI) and large language models (LLMs) unravel new capabilities for AI personal assistants to ... more
-4.1883 Towards Intelligent VR Training: A Physiological Adaptation Framework for Cognitive Load and Stress Detection
Authors: Mahsa Nasri | Date: 2025-04-11
Adaptive Virtual Reality (VR) systems have the potential to enhance training and learning experiences by dynamically responding to users' cognitive states. This research investigates how eye tracking and heart rate variability (HRV) can be used to detect cognitive load and stress in VR environments, ... more
-4.1887 Audio-visual Event Localization on Portrait Mode Short Videos
Authors: Wuyang Liu, Yi Chai, Yongpeng Yan, Yanzhen Ren | Date: 2025-04-11
Audio-visual event localization (AVEL) plays a critical role in multimodal scene understanding. While existing datasets for AVEL predominantly comprise landscape-oriented long videos with clean and simple audio context, short videos have become the primary format of online video content due to the t ... more
-4.1888 Automating Customer Needs Analysis: A Comparative Study of Large Language Models in the Travel Industry
Authors: Simone Barandoni, Filippo Chiarello, Lorenzo Cascone, Emiliano Marrale, Salvatore Puccio | Date: 2025-04-11
In the rapidly evolving landscape of Natural Language Processing (NLP), Large Language Models (LLMs) have emerged as powerful tools for many tasks, such as extracting valuable insights from vast amounts of textual data. In this study, we conduct a comparative analysis of LLMs for the extraction of t ... more
LLMs
-4.1954 Generative AI Enhanced Financial Risk Management Information Retrieval
Authors: Amin Haeri, Jonathan Vitrano, Mahdi Ghelichi | Date: 2025-04-11
Risk management in finance involves recognizing, evaluating, and addressing financial risks to maintain stability and ensure regulatory compliance. Extracting relevant insights from extensive regulatory documents is a complex challenge requiring advanced retrieval and language models. This paper int ... more
-4.1986 Teaching pathology foundation models to accurately predict gene expression with parameter efficient knowledge transfer
Authors: Shi Pan, Jianan Chen, Maria Secrier | Date: 2025-04-11
Gene expression profiling provides critical insights into cellular heterogeneity, biological processes and disease mechanisms. There has been an increasing interest in computational approaches that can predict gene expression directly from digitalized histopathology images. While image foundation mo ... more
-4.2009 Assessing employment and labour issues implicated by using AI
Authors: Thijs Willems, Darion Jin Hotan, Jiawen Cheryl Tang, Norakmal Hakim bin Norhashim, King Wang Poon, Zi An Galvyn Goh, Radha Vinod | Date: 2025-04-11
This chapter critiques the dominant reductionist approach in AI and work studies, which isolates tasks and skills as replaceable components. Instead, it advocates for a systemic perspective that emphasizes the interdependence of tasks, roles, and workplace contexts. Two complementary approaches are ... more
-4.2037 Differential Adjusted Parity for Learning Fair Representations
Authors: Bucher Sahyouni, Matthew Vowels, Liqun Chen, Simon Hadfield | Date: 2025-04-11
The development of fair and unbiased machine learning models remains an ongoing objective for researchers in the field of artificial intelligence. We introduce the Differential Adjusted Parity (DAP) loss to produce unbiased informative representations. It utilises a differentiable variant of the adj ... more
-4.207 Efficient Timestamping for Sampling-based Race Detection
Authors: Minjian Zhang, Daniel Wee Soong Lim, Mosaad Al Thokair, Umang Mathur, Mahesh Viswanathan | Date: 2025-04-11
Dynamic race detection based on the happens before (HB) partial order has now become the de facto approach to quickly identify data races in multi-threaded software. Most practical implementations for detecting these races use timestamps to infer causality between events and detect races based on th ... more
-4.2075 A Serendipitous Recommendation System Considering User Curiosity
Authors: Zhelin Xu, Atsushi Matsumura | Date: 2025-04-11
To address the problem of narrow recommendation ranges caused by an emphasis on prediction accuracy, serendipitous recommendations, which consider both usefulness and unexpectedness, have attracted attention. However, realizing serendipitous recommendations is challenging due to the varying proporti ... more
-4.2084 Sliced Wasserstein Discrepancy in Disentangling Representation and Adaptation Networks for Unsupervised Domain Adaptation
Authors: Joel Sol, Shadi Alijani, Homayoun Najjaran | Date: 2025-04-11
This paper introduces DRANet-SWD as a novel complete pipeline for disentangling content and style representations of images for unsupervised domain adaptation (UDA). The approach builds upon DRANet by incorporating the sliced Wasserstein discrepancy (SWD) as a style loss instead of the traditional G ... more
-4.2108 Compound and Parallel Modes of Tropical Convolutional Neural Networks
Authors: Mingbo Li, Liying Liu, Ye Luo | Date: 2025-04-11
Convolutional neural networks have become increasingly deep and complex, leading to higher computational costs. While tropical convolutional neural networks (TCNNs) reduce multiplications, they underperform compared to standard CNNs. To address this, we propose two new variants - compound TCNN (cTCN ... more
-4.2156 DIMA: DIffusing Motion Artifacts for unsupervised correction in brain MRI images
Authors: Paolo Angella, Luca Balbi, Fabrizio Ferrando, Paolo Traverso, Rosario Varriale, Vito Paolo Pastore, Matteo Santacesaria | Date: 2025-04-11
Motion artifacts remain a significant challenge in Magnetic Resonance Imaging (MRI), compromising diagnostic quality and potentially leading to misdiagnosis or repeated scans. Existing deep learning approaches for motion artifact correction typically require paired motion-free and motion-affected im ... more
-4.2193 Persona Dynamics: Unveiling the Impact of Personality Traits on Agents in Text-Based Games
Authors: Seungwon Lim, Seungbeen Lee, Dongjun Min, Youngjae Yu | Date: 2025-04-11
Artificial agents are increasingly central to complex interactions and decision-making tasks, yet aligning their behaviors with desired human values remains an open challenge. In this work, we investigate how human-like personality traits influence agent behavior and performance within text-based in ... more
-4.2229 GSta: Efficient Training Scheme with Siestaed Gaussians for Monocular 3D Scene Reconstruction
Authors: Anil Armagan, Albert Sa\`a-Garriga, Bruno Manganelli, Kyuwon Kim, M. Kerim Yucel | Date: 2025-04-11
Gaussian Splatting (GS) is a popular approach for 3D reconstruction, mostly due to its ability to converge reasonably fast, faithfully represent the scene and render (novel) views in a fast fashion. However, it suffers from large storage and memory requirements, and its training speed still lags beh ... more
-4.225 FAME: Introducing Fuzzy Additive Models for Explainable AI
Authors: Omer Bahadir Gokmen, Yusuf Guven, Tufan Kumbasar | Date: 2025-04-11
In this study, we introduce the Fuzzy Additive Model (FAM) and FAM with Explainability (FAME) as a solution for Explainable Artificial Intelligence (XAI). The family consists of three layers: (1) a Projection Layer that compresses the input space, (2) a Fuzzy Layer built upon Single Input-Single Out ... more
-4.2287 NeedleInATable: Exploring Long-Context Capability of Large Language Models towards Long-Structured Tables
Authors: Lanrui Wang, Mingyu Zheng, Hongyin Tang, Zheng Lin, Yanan Cao, Jingang Wang, Xunliang Cai, Weiping Wang | Date: 2025-04-11
Processing structured tabular data, particularly lengthy tables, constitutes a fundamental yet challenging task for large language models (LLMs). However, existing long-context benchmarks primarily focus on unstructured text, neglecting the challenges of long and complex structured tables. To addres ... more
-4.2368 Identifying Information from Observations with Uncertainty and Novelty
Authors: Derek S. Prijatelj (University of Notre Dame), Timothy J. Ireland (Independent Researcher), Walter J. Scheirer (University of Notre Dame) | Date: 2025-04-11
A machine learning tasks from observations must encounter and process uncertainty and novelty, especially when it is to maintain performance when observing new information and to choose the hypothesis that best fits the current observations. In this context, some key questions arise: what and how mu ... more
-4.2456 On the Effectiveness and Generalization of Race Representations for Debiasing High-Stakes Decisions
Authors: Dang Nguyen, Chenhao Tan | Date: 2025-04-11
Understanding and mitigating biases is critical for the adoption of large language models (LLMs) in high-stakes decision-making. We introduce Admissions and Hiring, decision tasks with hypothetical applicant profiles where a person's race can be inferred from their name, as simplified test beds for ... more
-4.2459 Clustering and novel class recognition: evaluating bioacoustic deep learning feature extractors
Authors: Vincent S. Kather, Burooj Ghani, Dan Stowell | Date: 2025-04-11
In computational bioacoustics, deep learning models are composed of feature extractors and classifiers. The feature extractors generate vector representations of the input sound segments, called embeddings, which can be input to a classifier. While benchmarking of classification scores provides insi ... more
-4.2528 TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version)
Authors: Zeliang Kan, Shae McFadden, Daniel Arp, Feargus Pendlebury, Roberto Jordaney, Johannes Kinder, Fabio Pierazzi, Lorenzo Cavallaro | Date: 2025-04-11
Machine learning (ML) plays a pivotal role in detecting malicious software. Despite the high F1-scores reported in numerous studies reaching upwards of 0.99, the issue is not completely solved. Malware detectors often experience performance decay due to constantly evolving operating systems and atta ... more
-4.2558 Understanding the Effect of Opinion Polarization in Short Video Browsing
Authors: Bangde Du, Ziyi Ye, Monika Jankowska, Zhijing Wu, Qingyao Ai, Yiqun Liu | Date: 2025-04-11
This paper explores the impact of Opinion Polarization (OP) in the increasingly prevalent context of short video browsing, a dominant medium in the contemporary digital landscape with significant influence on public opinion and social dynamics. We investigate the effects of OP on user perceptions an ... more
-4.2563 Center-fixing of tropical cyclones using uncertainty-aware deep learning applied to high-temporal-resolution geostationary satellite imagery
Authors: Ryan Lagerquist, Galina Chirokova, Robert DeMaria, Mark DeMaria, Imme Ebert-Uphoff | Date: 2025-04-11
Determining the location of a tropical cyclone's (TC) surface circulation center -- "center-fixing" -- is a critical first step in the TC-forecasting process, affecting current and future estimates of track, intensity, and structure. Despite a recent increase in automated center-fixing methods, only ... more
-4.2567 Hybrid Temporal Differential Consistency Autoencoder for Efficient and Sustainable Anomaly Detection in Cyber-Physical Systems
Authors: Michael Somma | Date: 2025-04-11
Cyberattacks on critical infrastructure, particularly water distribution systems, have increased due to rapid digitalization and the integration of IoT devices and industrial control systems (ICS). These cyber-physical systems (CPS) introduce new vulnerabilities, requiring robust and automated intru ... more
-4.262 Diffusion Factor Models: Generating High-Dimensional Returns with Factor Structure
Authors: Minshuo Chen, Renyuan Xu, Yumin Xu, Ruixun Zhang | Date: 2025-04-11
Financial scenario simulation is essential for risk management and portfolio optimization, yet it remains challenging especially in high-dimensional and small data settings common in finance. We propose a diffusion factor model that integrates latent factor structure into generative diffusion proces ... more
-4.2635 How do Copilot Suggestions Impact Developers' Frustration and Productivity?
Authors: Emanuela Guglielmi, Venera Arnoudova, Gabriele Bavota, Rocco Oliveto, Simone Scalabrino | Date: 2025-04-11
Context. AI-based development tools, such as GitHub Copilot, are transforming the software development process by offering real-time code suggestions. These tools promise to improve the productivity by reducing cognitive load and speeding up task completion. Previous exploratory studies, however, sh ... more
-4.2785 Exploring Ordinal Bias in Action Recognition for Instructional Videos
Authors: Joochan Kim, Minjoon Jung, Byoung-Tak Zhang | Date: 2025-04-11
Action recognition models have achieved promising results in understanding instructional videos. However, they often rely on dominant, dataset-specific action sequences rather than true video comprehension, a problem that we define as ordinal bias. To address this issue, we propose two effective vid ... more
-4.2798 Wheat3DGS: In-field 3D Reconstruction, Instance Segmentation and Phenotyping of Wheat Heads with Gaussian Splatting
Authors: Daiwei Zhang, Joaquin Gajardo, Tomislav Medic, Isinsu Katircioglu, Mike Boss, Norbert Kirchgessner, Achim Walter, Lukas Roth | Date: 2025-04-11
Automated extraction of plant morphological traits is crucial for supporting crop breeding and agricultural management through high-throughput field phenotyping (HTFP). Solutions based on multi-view RGB images are attractive due to their scalability and affordability, enabling volumetric measurement ... more
-4.2804 Spectrum Sharing by Space-Time Waveform Shaping
Authors: Hatef Nouri, George Sklivanitis, Dimitris A. Pados, Elizabeth Serena Bentley | Date: 2025-04-11
In this paper, we consider the task of introducing a new wireless data link over a given occupied frequency band using a multi-antenna transmitter and receiver. We design formally a dynamic multiple-input multiple-output (MIMO) wireless link that can coexist in the fixed congested frequency band by ... more
-4.3048 Joint Group Profiling and Recommendation via Deep Neural Network-based Multi-Task Learning
Authors: Ngoc Luyen Le, Marie-H\'el\`ene Abel | Date: 2025-04-11
Group recommender systems aim to generate recommendations that align with the collective preferences of a group, introducing challenges that differ significantly from those in individual recommendation scenarios. This paper presents Joint Group Profiling and Recommendation via Deep Neural Network-ba ... more
-4.3064 Reflection on Code Contributor Demographics and Collaboration Patterns in the Rust Community
Authors: Rohit Dandamudi, Ifeoma Adaji, Gema Rodr\'iguez-P\'erez | Date: 2025-04-11
Open-source software communities thrive on global collaboration and contributions from diverse participants. This study explores the Rust programming language ecosystem to understand its contributors' demographic composition and interaction patterns. Our objective is to investigate the phenomenon of ... more
-4.3076 Detect All-Type Deepfake Audio: Wavelet Prompt Tuning for Enhanced Auditory Perception
Authors: Yuankun Xie, Ruibo Fu, Zhiyong Wang, Xiaopeng Wang, Songjun Cao, Long Ma, Haonan Cheng, Long Ye | Date: 2025-04-11
The rapid advancement of audio generation technologies has escalated the risks of malicious deepfake audio across speech, sound, singing voice, and music, threatening multimedia security and trust. While existing countermeasures (CMs) perform well in single-type audio deepfake detection (ADD), their ... more
-4.3088 An evolving surface finite element method for the Cahn-Hilliard equation with a logarithmic potential
Authors: Charles M. Elliott, Thomas Sales | Date: 2025-04-11
In this paper we study semi-discrete and fully discrete evolving surface finite element schemes for the Cahn-Hilliard equation with a logarithmic potential. Specifically we consider linear finite elements discretising space and backward Euler time discretisation. Our analysis relies on a specific ge ... more
-4.3091 On the Loewner framework, the Kolmogorov superposition theorem, and the curse of dimensionality
Authors: Athanasios C. Antoulas, Ion Victor Gosea, Charles Poussot-Vassal | Date: 2025-04-11
The Loewner framework is an interpolatory approach for the approximation of linear and nonlinear systems. The purpose here is to extend this framework to linear parametric systems with an arbitrary number n of parameters. To achieve this, a new generalized multivariate rational function realization ... more
-4.316 Generalized Semantic Contrastive Learning via Embedding Side Information for Few-Shot Object Detection
Authors: Ruoyu Chen, Hua Zhang, Jingzhi Li, Li Liu, Zhen Huang, Xiaochun Cao | Date: 2025-04-11
The objective of few-shot object detection (FSOD) is to detect novel objects with few training samples. The core challenge of this task is how to construct a generalized feature space for novel categories with limited data on the basis of the base category space, which could adapt the learned detect ... more
-4.3192 Measuring the Discrepancy between 3D Geometric Models using Directional Distance Fields
Authors: Siyu Ren, Junhui Hou, Xiaodong Chen, Hongkai Xiong, Wenping Wang | Date: 2025-04-11
Qualifying the discrepancy between 3D geometric models, which could be represented with either point clouds or triangle meshes, is a pivotal issue with board applications. Existing methods mainly focus on directly establishing the correspondence between two models and then aggregating point-wise dis ... more
-4.3217 TASTE: Text-Aligned Speech Tokenization and Embedding for Spoken Language Modeling
Authors: Liang-Hsuan Tseng, Yi-Chang Chen, Kuan-Yi Lee, Da-Shan Shiu, Hung-yi Lee | Date: 2025-04-11
Large Language Models (LLMs) excel in text-based natural language processing tasks but remain constrained by their reliance on textual inputs and outputs. To enable more natural human-LLM interaction, recent progress have focused on deriving a spoken language model (SLM) that can not only listen but ... more
-4.3254 The membership problem for constant-sized quantum correlations is undecidable
Authors: Honghao Fu, Carl A. Miller, William Slofstra | Date: 2025-04-11
When two spatially separated parties make measurements on an unknown entangled quantum state, what correlations can they achieve? How difficult is it to determine whether a given correlation is a quantum correlation? These questions are central to problems in quantum communication and computation. P ... more
-4.3255 Finite Field Multiple Access III: from 2-ary to p-ary
Authors: Qi-yue Yu | Date: 2025-04-11
This paper extends finite-field multiple-access (FFMA) techniques from binary to general $p$-ary source transmission. We introduce element-assemblage (EA) codes over GF($p^m$), generalizing element-pair (EP) codes, and define two specific types for ternary transmission: orthogonal EA codes and doubl ... more
-4.3258 asKAN: Active Subspace embedded Kolmogorov-Arnold Network
Authors: Zhiteng Zhou, Zhaoyue Xu, Yi Liu, Shizhao Wang | Date: 2025-04-11
The Kolmogorov-Arnold Network (KAN) has emerged as a promising neural network architecture for small-scale AI+Science applications. However, it suffers from inflexibility in modeling ridge functions, which is widely used in representing the relationships in physical systems. This study investigates ... more
-4.3333 Verifying Equilibria in Finite-Horizon Probabilistic Concurrent Game Systems
Authors: Senthil Rajasekaran, Moshe Y. Vardi | Date: 2025-04-11
Finite-horizon probabilistic multiagent concurrent game systems, also known as finite multiplayer stochastic games, are a well-studied model in computer science due to their ability to represent a wide range of real-world scenarios involving strategic interactions among agents over a finite amount o ... more
-4.3339 Towards Collaborative Autonomous Driving: Simulation Platform and End-to-End System
Authors: Genjia Liu, Yue Hu, Chenxin Xu, Weibo Mao, Junhao Ge, Zhengxiang Huang, Yifan Lu, Yinda Xu, Junkai Xia, Yafei Wang, Siheng Chen | Date: 2025-04-11
Vehicle-to-everything-aided autonomous driving (V2X-AD) has a huge potential to provide a safer driving solution. Despite extensive researches in transportation and communication to support V2X-AD, the actual utilization of these infrastructures and communication resources in enhancing driving perfo ... more
-4.3349 LostPaw: Finding Lost Pets using a Contrastive Learning-based Transformer with Visual Input
Authors: Andrei Voinea, Robin Kock, Maruf A. Dhali | Date: 2025-04-11
Losing pets can be highly distressing for pet owners, and finding a lost pet is often challenging and time-consuming. An artificial intelligence-based application can significantly improve the speed and accuracy of finding lost pets. To facilitate such an application, this study introduces a contras ... more
-4.3363 LCGC: Learning from Consistency Gradient Conflicting for Class-Imbalanced Semi-Supervised Debiasing
Authors: Weiwei Xing, Yue Cheng, Hongzhu Yi, Xiaohui Gao, Xiang Wei, Xiaoyu Guo, Yuming Zhang, Xinyu Pang | Date: 2025-04-11
Classifiers often learn to be biased corresponding to the class-imbalanced dataset, especially under the semi-supervised learning (SSL) set. While previous work tries to appropriately re-balance the classifiers by subtracting a class-irrelevant image's logit, but lacks a firm theoretical basis. We t ... more
-4.3364 Dolphin: Moving Towards Closed-loop Auto-research through Thinking, Practice, and Feedback
Authors: Jiakang Yuan, Xiangchao Yan, Shiyang Feng, Bo Zhang, Tao Chen, Botian Shi, Wanli Ouyang, Yu Qiao, Lei Bai, Bowen Zhou | Date: 2025-04-11
The scientific research paradigm is undergoing a profound transformation owing to the development of Artificial Intelligence (AI). Recent works demonstrate that various AI-assisted research methods can largely improve research efficiency by improving data analysis, accelerating computation, and fost ... more
-4.3367 Digital Gene: Learning about the Physical World through Analytic Concepts
Authors: Jianhua Sun, Cewu Lu | Date: 2025-04-11
Reviewing the progress in artificial intelligence over the past decade, various significant advances (e.g. object detection, image generation, large language models) have enabled AI systems to produce more semantically meaningful outputs and achieve widespread adoption in internet scenarios. Neverth ... more
-4.3428 $\Pi$-NeSy: A Possibilistic Neuro-Symbolic Approach
Authors: Isma\"il Baaj, Pierre Marquis | Date: 2025-04-11
In this article, we introduce a neuro-symbolic approach that combines a low-level perception task performed by a neural network with a high-level reasoning task performed by a possibilistic rule-based system. The goal is to be able to derive for each input instance the degree of possibility that it ... more
-4.3507 Robust Capacity Expansion Modelling for Renewable Energy Systems under Weather and Demand Uncertainty
Authors: Sebastian Kebrich, Felix Engelhardt, David Franzmann, Christina B\"using, Jochen Lin{\ss}en, Heidi Heinrichs | Date: 2025-04-11
Future greenhouse gas neutral energy systems will be dominated by variable renewable energy technologies. However, renewable electricity generation from wind and solar technologies, as well as electricity demand, varies with the weather. This work addresses the problem of determining optimal capacit ... more
-4.354 No Detail Left Behind: Revisiting Self-Retrieval for Fine-Grained Image Captioning
Authors: Manu Gaur, Darshan Singh, Makarand Tapaswi | Date: 2025-04-11
Image captioning systems are unable to generate fine-grained captions as they are trained on data that is either noisy (alt-text) or generic (human annotations). This is further exacerbated by maximum likelihood training that encourages generation of frequently occurring phrases. Previous works have ... more
-4.3573 Analyzing the Impact of Low-Rank Adaptation for Cross-Domain Few-Shot Object Detection in Aerial Images
Authors: Hicham Talaoubrid, Anissa Mokraoui, Ismail Ben Ayed, Axel Prouvost, Sonimith Hang, Monit Korn, R\'emi Harvey | Date: 2025-04-11
This paper investigates the application of Low-Rank Adaptation (LoRA) to small models for cross-domain few-shot object detection in aerial images. Originally designed for large-scale models, LoRA helps mitigate overfitting, making it a promising approach for resource-constrained settings. We integra ... more
-4.3605 Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization
Authors: Yury Demidovich, Petr Ostroukhov, Grigory Malinovsky, Samuel Horv\'ath, Martin Tak\'a\v{c}, Peter Richt\'arik, Eduard Gorbunov | Date: 2025-04-11
Non-convex Machine Learning problems typically do not adhere to the standard smoothness assumption. Based on empirical findings, Zhang et al. (2020b) proposed a more realistic generalized $(L_0, L_1)$-smoothness assumption, though it remains largely unexplored. Many existing algorithms designed for ... more
Federated Learning
-4.3671 GWQ: Gradient-Aware Weight Quantization for Large Language Models
Authors: Yihua Shao, Yan Gu, Siyu Chen, Haiyang Liu, Zijian Ling, Minxi Yan, Ziyang Yan, Chenyu Zhang, Michele Magno, Haotong Qin, Yan Wang, Jingcai Guo, Ling Shao, Hao Tang | Date: 2025-04-11
Large language models (LLMs) show impressive performance in solving complex language tasks. However, its large number of parameters presents significant challenges for the deployment. So, compressing LLMs to low bits can enable to deploy on resource-constrained devices. To address this problem, we p ... more
-4.3694 Developing Modular Grasping and Manipulation Pipeline Infrastructure to Streamline Performance Benchmarking
Authors: Brian Flynn, Kostas Bekris, Berk Calli, Aaron Dollar, Adam Norton, Yu Sun, Holly Yanco | Date: 2025-04-11
The robot manipulation ecosystem currently faces issues with integrating open-source components and reproducing results. This limits the ability of the community to benchmark and compare the performance of different solutions to one another in an effective manner, instead relying on largely holistic ... more
-4.3794 The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Data
Authors: Georgi Ganev, Meenatchi Sundaram Muthu Selva Annamalai, Sofiane Mahiou, Emiliano De Cristofaro | Date: 2025-04-11
Differentially Private (DP) generative marginal models are often used in the wild to release synthetic tabular datasets in lieu of sensitive data while providing formal privacy guarantees. These models approximate low-dimensional marginals or query workloads; crucially, they require the training dat ... more
-4.3818 GenDoP: Auto-regressive Camera Trajectory Generation as a Director of Photography
Authors: Mengchen Zhang, Tong Wu, Jing Tan, Ziwei Liu, Gordon Wetzstein, Dahua Lin | Date: 2025-04-11
Camera trajectory design plays a crucial role in video production, serving as a fundamental tool for conveying directorial intent and enhancing visual storytelling. In cinematography, Directors of Photography meticulously craft camera movements to achieve expressive and intentional framing. However, ... more
-4.384 Replacing Paths with Connection-Biased Attention for Knowledge Graph Completion
Authors: Sharmishtha Dutta, Alex Gittens, Mohammed J. Zaki, Charu C. Aggarwal | Date: 2025-04-11
Knowledge graph (KG) completion aims to identify additional facts that can be inferred from the existing facts in the KG. Recent developments in this field have explored this task in the inductive setting, where at test time one sees entities that were not present during training; the most performan ... more
-4.3848 Probabilistic Grading and Classification System for End-of-Life Building Components Toward Circular Economy Loop
Authors: Yiping Meng, Sergio Cavalaro, Mohamed Osmani | Date: 2025-04-11
The longevity and viability of construction components in a circular economy demand a robust, data-informed framework for reuse decision-making. This paper introduces a multi-level grading and classification system that combines Bayesian probabilistic modeling with scenario-based performance thresho ... more
-4.3915 Leveraging LLMs for User Stories in AI Systems: UStAI Dataset
Authors: Asma Yamani, Malak Baslyman, Moataz Ahmed | Date: 2025-04-11
AI systems are gaining widespread adoption across various sectors and domains. Creating high-quality AI system requirements is crucial for aligning the AI system with business goals and consumer values and for social responsibility. However, with the uncertain nature of AI systems and the heavy reli ... more
-4.406 LLM Safeguard is a Double-Edged Sword: Exploiting False Positives for Denial-of-Service Attacks
Authors: Qingzhao Zhang, Ziyang Xiong, Z. Morley Mao | Date: 2025-04-11
Safety is a paramount concern for large language models (LLMs) in open deployment, motivating the development of safeguard methods that enforce ethical and responsible use through safety alignment or guardrail mechanisms. Jailbreak attacks that exploit the \emph{false negatives} of safeguard methods ... more
-4.4065 Adaptive Computation Pruning for the Forgetting Transformer
Authors: Zhixuan Lin, Johan Obando-Ceron, Xu Owen He, Aaron Courville | Date: 2025-04-11
The recently proposed Forgetting Transformer (FoX) incorporates a forget gate into softmax attention and has shown consistently better or on-par performance compared to the standard RoPE-based Transformer. Notably, many attention heads in FoX tend to forget quickly, causing their output at each time ... more
-4.4066 Mesh Mamba: A Unified State Space Model for Saliency Prediction in Non-Textured and Textured Meshes
Authors: Kaiwei Zhang, Dandan Zhu, Xiongkuo Min, Guangtao Zhai | Date: 2025-04-11
Mesh saliency enhances the adaptability of 3D vision by identifying and emphasizing regions that naturally attract visual attention. To investigate the interaction between geometric structure and texture in shaping visual attention, we establish a comprehensive mesh saliency dataset, which is the fi ... more
-4.4129 Accelerating LLM Inference Throughput via Asynchronous KV Cache Prefetching
Authors: Yanhao Dong, Yubo Miao, Weinan Li, Xiao Zheng, Chao Wang, Feng Lyu | Date: 2025-04-11
Large Language Models (LLMs) exhibit pronounced memory-bound characteristics during inference due to High Bandwidth Memory (HBM) bandwidth constraints. In this paper, we propose an L2 Cache-oriented asynchronous KV Cache prefetching method to break through the memory bandwidth bottleneck in LLM infe ... more
LLMs
-4.4161 A Multi-Modal Interaction Framework for Efficient Human-Robot Collaborative Shelf Picking
Authors: Abhinav Pathak, Kalaichelvi Venkatesan, Tarek Taha, Rajkumar Muthusamy | Date: 2025-04-11
The growing presence of service robots in human-centric environments, such as warehouses, demands seamless and intuitive human-robot collaboration. In this paper, we propose a collaborative shelf-picking framework that combines multimodal interaction, physics-based reasoning, and task division for e ... more
-4.4176 Low-Rank Thinning
Authors: Annabelle Michael Carrell, Albert Gong, Abhishek Shetty, Raaz Dwivedi, Lester Mackey | Date: 2025-04-11
The goal in thinning is to summarize a dataset using a small set of representative points. Remarkably, sub-Gaussian thinning algorithms like Kernel Halving and Compress can match the quality of uniform subsampling while substantially reducing the number of summary points. However, existing guarantee ... more
-4.4232 Controllable Automatic Foley Artist
Authors: Roi Benita, Michael Finkelson, Tavi Halperin, Gleb Sterkin, Yossi Adi | Date: 2025-04-11
Foley is a key element in video production, refers to the process of adding an audio signal to a silent video while ensuring semantic and temporal alignment. In recent years, the rise of personalized content creation and advancements in automatic video-to-audio models have increased the demand for g ... more
-4.4305 A Graph Diffusion Algorithm for Lexical Similarity Evaluation
Authors: Karol Mikula, Mariana Sarkociov\'a Reme\v{s}\'ikov\'a | Date: 2025-04-11
In this paper, we present an algorithm for evaluating lexical similarity between a given language and several reference language clusters. As an input, we have a list of concepts and the corresponding translations in all considered languages. Moreover, each reference language is assigned to one of $ ... more
-4.4347 A Deep Generative Learning Approach for Two-stage Adaptive Robust Optimization
Authors: Aron Brenner, Rahman Khorramfar, Jennifer Sun, Saurabh Amin | Date: 2025-04-11
Two-stage adaptive robust optimization (ARO) is a powerful approach for planning under uncertainty, balancing first-stage decisions with recourse decisions made after uncertainty is realized. To account for uncertainty, modelers typically define a simple uncertainty set over which potential outcomes ... more
-4.4374 CodeMMLU: A Multi-Task Benchmark for Assessing Code Understanding & Reasoning Capabilities of CodeLLMs
Authors: Dung Nguyen Manh, Thang Phan Chau, Nam Le Hai, Thong T. Doan, Nam V. Nguyen, Quang Pham, Nghi D. Q. Bui | Date: 2025-04-11
Recent advances in Code Large Language Models (CodeLLMs) have primarily focused on open-ended code generation, often overlooking the crucial aspect of code understanding and reasoning. To bridge this gap, we introduce CodeMMLU, a comprehensive multiple-choice benchmark designed to evaluate the depth ... more
-4.4394 The Writing is on the Wall: Analyzing the Boom of Inscriptions and its Impact on EVM-compatible Blockchains
Authors: Johnnatan Messias, Krzysztof Gogol, Maria In\^es Silva, Benjamin Livshits | Date: 2025-04-11
This paper examines inscription-related transactions on Ethereum and major EVM-compatible rollups, assessing their impact on scalability during transaction surges. Our results show that, on certain days, inscriptions accounted for nearly 90% of transactions on Arbitrum and ZKsync Era, while 53% on E ... more
-4.4402 From Stability to Inconsistency: A Study of Moral Preferences in LLMs
Authors: Monika Jotautaite, Mary Phuong, Chatrik Singh Mangat, Maria Angelica Martinez | Date: 2025-04-11
As large language models (LLMs) increasingly integrate into our daily lives, it becomes crucial to understand their implicit biases and moral tendencies. To address this, we introduce a Moral Foundations LLM dataset (MFD-LLM) grounded in Moral Foundations Theory, which conceptualizes human morality ... more
-4.4433 TSP-OCS: A Time-Series Prediction for Optimal Camera Selection in Multi-Viewpoint Surgical Video Analysis
Authors: Xinyu Liu, Xiaoguang Lin, Xiang Liu, Yong Yang, Hongqian Wang, Qilong Sun | Date: 2025-04-11
Recording the open surgery process is essential for educational and medical evaluation purposes; however, traditional single-camera methods often face challenges such as occlusions caused by the surgeon's head and body, as well as limitations due to fixed camera angles, which reduce comprehensibilit ... more
-4.445 Unraveling Human-AI Teaming: A Review and Outlook
Authors: Bowen Lou, Tian Lu, T. S. Raghu, Yingjie Zhang | Date: 2025-04-11
Artificial Intelligence (AI) is advancing at an unprecedented pace, with clear potential to enhance decision-making and productivity. Yet, the collaborative decision-making process between humans and AI remains underdeveloped, often falling short of its transformative possibilities. This paper explo ... more
-4.4619 Enhancing Metabolic Syndrome Prediction with Hybrid Data Balancing and Counterfactuals
Authors: Sanyam Paresh Shah, Abdullah Mamun, Shovito Barua Soumma, Hassan Ghasemzadeh | Date: 2025-04-11
Metabolic Syndrome (MetS) is a cluster of interrelated risk factors that significantly increases the risk of cardiovascular diseases and type 2 diabetes. Despite its global prevalence, accurate prediction of MetS remains challenging due to issues such as class imbalance, data scarcity, and methodolo ... more
-4.4622 CW-BASS: Confidence-Weighted Boundary-Aware Learning for Semi-Supervised Semantic Segmentation
Authors: Ebenezer Tarubinga, Jenifer Kalafatovich, Seong-Whan Lee | Date: 2025-04-11
Semi-supervised semantic segmentation (SSSS) aims to improve segmentation performance by utilizing large amounts of unlabeled data with limited labeled samples. Existing methods often suffer from coupling, where over-reliance on initial labeled data leads to suboptimal learning; confirmation bias, w ... more
-4.4625 Application of Quantum Approximate Optimization Algorithm in Solving the Total Domination Problem
Authors: Haoqian Pan, Shiyue Wang, Changhong Lu | Date: 2025-04-11
Recent advancements in quantum computing have led to significant research into applying quantum algorithms to combinatorial optimization problems. Among these challenges, the Total Domination Problem (TDP) is particularly noteworthy, representing a classic and critical example in the field. Since th ... more
-4.4647 Handling LP-Rounding for Hierarchical Clustering and Fitting Distances by Ultrametrics
Authors: Hyung-Chan An, Mong-Jen Kao, Changyeol Lee, Mu-Ting Lee | Date: 2025-04-11
We consider the classic correlation clustering problem in the hierarchical setting. Given a complete graph $G=(V,E)$ and $\ell$ layers of input information, where the input of each layer consists of a nonnegative weight and a labeling of the edges with either + or -, this problem seeks to compute fo ... more
-4.466 A Simple but Strong Baseline for Sounding Video Generation: Effective Adaptation of Audio and Video Diffusion Models for Joint Generation
Authors: Masato Ishii, Akio Hayakawa, Takashi Shibuya, Yuki Mitsufuji | Date: 2025-04-11
In this work, we build a simple but strong baseline for sounding video generation. Given base diffusion models for audio and video, we integrate them with additional modules into a single model and train it to make the model jointly generate audio and video. To enhance alignment between audio-video ... more
-4.469 TabRep: a Simple and Effective Continuous Representation for Training Tabular Diffusion Models
Authors: Jacob Si, Zijing Ou, Mike Qu, Zhengrui Xiang, Yingzhen Li | Date: 2025-04-11
Diffusion models have been the predominant generative model for tabular data generation. However, they face the conundrum of modeling under a separate versus a unified data representation. The former encounters the challenge of jointly modeling all multi-modal distributions of tabular data in one mo ... more
-4.4761 Ice-Breakers, Turn-Takers and Fun-Makers: Exploring Robots for Groups with Teenagers
Authors: Sarah Gillet, Katie Winkle, Giulia Belgiovine, Iolanda Leite | Date: 2025-04-11
Successful, enjoyable group interactions are important in public and personal contexts, especially for teenagers whose peer groups are important for self-identity and self-esteem. Social robots seemingly have the potential to positively shape group interactions, but it seems difficult to effect such ... more
-4.478 Analyzing Examinee Comments using DistilBERT and Machine Learning to Ensure Quality Control in Exam Content
Authors: Ye (Cheryl), Ma | Date: 2025-04-11
This study explores using Natural Language Processing (NLP) to analyze candidate comments for identifying problematic test items. We developed and validated machine learning models that automatically identify relevant negative feedback, evaluated approaches of incorporating psychometric features enh ... more
-4.4791 FreeCloth: Free-form Generation Enhances Challenging Clothed Human Modeling
Authors: Hang Ye, Xiaoxuan Ma, Hai Ci, Wentao Zhu, Yizhou Wang | Date: 2025-04-11
Achieving realistic animated human avatars requires accurate modeling of pose-dependent clothing deformations. Existing learning-based methods heavily rely on the Linear Blend Skinning (LBS) of minimally-clothed human models like SMPL to model deformation. However, they struggle to handle loose clot ... more
-4.4861 Assumption-free fidelity bounds for hardware noise characterization
Authors: Nicolo Colombo | Date: 2025-04-11
In the Quantum Supremacy regime, quantum computers may overcome classical machines on several tasks if we can estimate, mitigate, or correct unavoidable hardware noise. Estimating the error requires classical simulations, which become unfeasible in the Quantum Supremacy regime. We leverage Machine L ... more
-4.4867 Can LLMs Simulate Personas with Reversed Performance? A Benchmark for Counterfactual Instruction Following
Authors: Sai Adith Senthil Kumar, Hao Yan, Saipavan Perepa, Murong Yue, Ziyu Yao | Date: 2025-04-11
Large Language Models (LLMs) are now increasingly widely used to simulate personas in virtual environments, leveraging their instruction-following capability. However, we discovered that even state-of-the-art LLMs cannot simulate personas with reversed performance (e.g., student personas with low pr ... more
-4.4878 NAPER: Fault Protection for Real-Time Resource-Constrained Deep Neural Networks
Authors: Rian Adam Rajagede, Muhammad Husni Santriaji, Muhammad Arya Fikriansyah, Hilal Hudan Nuha, Yanjie Fu, Yan Solihin | Date: 2025-04-11
Fault tolerance in Deep Neural Networks (DNNs) deployed on resource-constrained systems presents unique challenges for high-accuracy applications with strict timing requirements. Memory bit-flips can severely degrade DNN accuracy, while traditional protection approaches like Triple Modular Redundanc ... more
-4.494 FedMerge: Federated Personalization via Model Merging
Authors: Shutong Chen, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang | Date: 2025-04-11
One global model in federated learning (FL) might not be sufficient to serve many clients with non-IID tasks and distributions. While there has been advances in FL to train multiple global models for better personalization, they only provide limited choices to clients so local finetuning is still in ... more
Federated Learning
-4.4945 Levels of Binary Equivalence for the Comparison of Binaries from Alternative Builds
Authors: Jens Dietrich, Tim White, Behnaz Hassanshahi, Paddy Krishnan | Date: 2025-04-11
In response to challenges in software supply chain security, several organisations have created infrastructures to independently build commodity open source projects and release the resulting binaries. Build platform variability can strengthen security as it facilitates the detection of compromised ... more
-4.4951 PointNorm-Net: Self-Supervised Normal Prediction of 3D Point Clouds via Multi-Modal Distribution Estimation
Authors: Jie Zhang, Minghui Nie, Changqing Zou, Jian Liu, Ligang Liu, Junjie Cao | Date: 2025-04-11
Although supervised deep normal estimators have recently shown impressive results on synthetic benchmarks, their performance deteriorates significantly in real-world scenarios due to the domain gap between synthetic and real data. Building high-quality real training data to boost those supervised me ... more
-4.4966 Human Trust in AI Search: A Large-Scale Experiment
Authors: Haiwen Li, Sinan Aral | Date: 2025-04-11
Large Language Models (LLMs) increasingly power generative search engines which, in turn, drive human information seeking and decision making at scale. The extent to which humans trust generative artificial intelligence (GenAI) can therefore influence what we buy, how we vote and our health. Unfortu ... more
-4.5005 Optimizing Large Language Models: Metrics, Energy Efficiency, and Case Study Insights
Authors: Tahniat Khan, Soroor Motie, Sedef Akinli Kocak, Shaina Raza | Date: 2025-04-11
The rapid adoption of large language models (LLMs) has led to significant energy consumption and carbon emissions, posing a critical challenge to the sustainability of generative AI technologies. This paper explores the integration of energy-efficient optimization techniques in the deployment of LLM ... more
LLMs
-4.5041 Symmetrizable systems
Authors: Hamed Taghavian, Jens Sj\"olund | Date: 2025-04-11
Transforming an asymmetric system into a symmetric system makes it possible to exploit the simplifying properties of symmetry in control problems. We define and characterize the family of symmetrizable systems, which can be transformed into symmetric systems by a linear transformation of their input ... more
-4.5133 ChatGPT-4 in the Turing Test: A Critical Analysis
Authors: Marco Giunti | Date: 2025-04-11
This paper critically examines the recent publication "ChatGPT-4 in the Turing Test" by Restrepo Echavarr\'ia (2025), challenging its central claims regarding the absence of minimally serious test implementations and the conclusion that ChatGPT-4 fails the Turing Test. The analysis reveals that the ... more
LLMs
-4.5136 Optimal promotions of new products on networks
Authors: Gadi Fibich, Amit Golan | Date: 2025-04-11
We present a novel methodology for analyzing the optimal promotion in the Bass model for the spreading of new products on networks. For general networks with $M$ nodes, the optimal promotion is the solution of $2^M-1$ nonlinearly-coupled boundary-value problems. On structured networks, however, the ... more
-4.5218 Reliability Assessment of Low-Cost PM Sensors under High Humidity and High PM Level Outdoor Conditions
Authors: Gulshan Kumar, Prasannaa Kumar D, Jay Dhariwal, Seshan Srirangarajan | Date: 2025-04-11
Low-cost particulate matter (PM) sensors have become increasingly popular due to their compact size, low power consumption, and cost-effective installation and maintenance. While several studies have explored the effects of meteorological conditions and pollution exposure on low-cost sensor (LCS) pe ... more
-4.5225 Literature Review: Cyber Security Monitoring in Maritime
Authors: Risto Vaarandi, Leonidas Tsiopoulos, Gabor Visky, Muaan Ur Rehman, Hayretdin Bahsi | Date: 2025-04-11
In recent years, many cyber incidents have occurred in the maritime sector, targeting the information technology (IT) and operational technology (OT) infrastructure. Although several literature review papers have been published in the maritime field, none of the previous studies has focused on cyber ... more
-4.5377 A Krylov projection algorithm for large symmetric matrices with dense spectra
Authors: Vladimir Druskin J\"orn Zimmerling | Date: 2025-04-11
We consider the approximation of $B^T (A+sI)^{-1} B$ for large s.p.d. $A\in\mathbb{R}^{n\times n}$ with dense spectrum and $B\in\mathbb{R}^{n\times p}$, $p\ll n$. We target the computations of Multiple-Input Multiple-Output (MIMO) transfer functions for large-scale discretizations of problems with c ... more
-4.5391 Semantically Safe Robot Manipulation: From Semantic Scene Understanding to Motion Safeguards
Authors: Lukas Brunke, Yanni Zhang, Ralf R\"omer, Jack Naimer, Nikola Staykov, Siqi Zhou, Angela P. Schoellig | Date: 2025-04-11
Ensuring safe interactions in human-centric environments requires robots to understand and adhere to constraints recognized by humans as "common sense" (e.g., "moving a cup of water above a laptop is unsafe as the water may spill" or "rotating a cup of water is unsafe as it can lead to pouring its c ... more
-4.5408 Design2GarmentCode: Turning Design Concepts to Tangible Garments Through Program Synthesis
Authors: Feng Zhou, Ruiyang Liu, Chen Liu, Gaofeng He, Yong-Lu Li, Xiaogang Jin, Huamin Wang | Date: 2025-04-11
Sewing patterns, the essential blueprints for fabric cutting and tailoring, act as a crucial bridge between design concepts and producible garments. However, existing uni-modal sewing pattern generation models struggle to effectively encode complex design concepts with a multi-modal nature and corre ... more
-4.5434 Are Vision-Language Models Ready for Dietary Assessment? Exploring the Next Frontier in AI-Powered Food Image Recognition
Authors: Sergio Romero-Tapiador, Ruben Tolosana, Blanca Lacruz-Pleguezuelos, Laura Judith Marcos Zambrano, Guadalupe X. Baz\'an, Isabel Espinosa-Salinas, Julian Fierrez, Javier Ortega-Garcia, Enrique Carrillo de Santa Pau, Aythami Morales | Date: 2025-04-11
Automatic dietary assessment based on food images remains a challenge, requiring precise food detection, segmentation, and classification. Vision-Language Models (VLMs) offer new possibilities by integrating visual and textual reasoning. In this study, we evaluate six state-of-the-art VLMs (ChatGPT, ... more
-4.5444 ASRL:A robust loss function with potential for development
Authors: Chenyu Hui, Anran Zhang, Xintong Li | Date: 2025-04-11
In this article, we proposed a partition:wise robust loss function based on the previous robust loss function. The characteristics of this loss function are that it achieves high robustness and a wide range of applicability through partition-wise design and adaptive parameter adjustment. Finally, th ... more
-4.5458 Outlier dimensions favor frequent tokens in language models
Authors: Iuri Macocco, Nora Graichen, Gemma Boleda, Marco Baroni | Date: 2025-04-11
We study last-layer outlier dimensions, i.e. dimensions that display extreme activations for the majority of inputs. We show that outlier dimensions arise in many different modern language models, and trace their function back to the heuristic of constantly predicting frequent words. We further show ... more
-4.5475 Quantized symbolic time series approximation
Authors: Erin Carson, Xinye Chen, Cheng Kang | Date: 2025-04-11
Time series are ubiquitous in numerous science and engineering domains, e.g., signal processing, bioinformatics, and astronomy. Previous work has verified the efficacy of symbolic time series representation in a variety of engineering applications due to its storage efficiency and numerosity reducti ... more
-4.5477 Reducing Formal Context Extraction: A Newly Proposed Framework from Big Corpora
Authors: Bryar A. Hassan, Shko M. Qader, Alla A. Hassan, Joan Lu, Aram M. Ahmed, Jafar Majidpour, Tarik A. Rashid | Date: 2025-04-11
Automating the extraction of concept hierarchies from free text is advantageous because manual generation is frequently labor- and resource-intensive. Free result, the whole procedure for concept hierarchy learning from free text entails several phases, including sentence-level text processing, sent ... more
-4.5532 Attributes-aware Visual Emotion Representation Learning
Authors: Rahul Singh Maharjan, Marta Romeo, Angelo Cangelosi | Date: 2025-04-11
Visual emotion analysis or recognition has gained considerable attention due to the growing interest in understanding how images can convey rich semantics and evoke emotions in human perception. However, visual emotion analysis poses distinctive challenges compared to traditional vision tasks, espec ... more
-4.561 Hybrid machine learning models based on physical patterns to accelerate CFD simulations: a short guide on autoregressive models
Authors: Arindam Sengupta, Rodrigo Abad\'ia-Heredia, Ashton Hetherington, Jos\'e Miguel P\'erez, Soledad Le Clainche | Date: 2025-04-11
Accurate modeling of the complex dynamics of fluid flows is a fundamental challenge in computational physics and engineering. This study presents an innovative integration of High-Order Singular Value Decomposition (HOSVD) with Long Short-Term Memory (LSTM) architectures to address the complexities ... more
-4.5629 Leveraging Non-Steady-State Frequency-Domain Data in Willems' Fundamental Lemma
Authors: T. J. Meijer, M. Wind, V. S. Dolk, W. P. M. H. Heemels | Date: 2025-04-11
Willems' fundamental lemma enables data-driven analysis and control by characterizing an unknown system's behavior directly in terms of measured data. In this work, we extend a recent frequency-domain variant of this result--previously limited to steady-state data--to incorporate non-steady-state da ... more
-4.5667 Predicting Survivability of Cancer Patients with Metastatic Patterns Using Explainable AI
Authors: Polycarp Nalela, Deepthi Rao, Praveen Rao | Date: 2025-04-11
Cancer remains a leading global health challenge and a major cause of mortality. This study leverages machine learning (ML) to predict the survivability of cancer patients with metastatic patterns using the comprehensive MSK-MET dataset, which includes genomic and clinical data from 25,775 patients ... more
-4.5817 Quantum neural networks facilitating quantum state classification
Authors: Diksha Sharma, Vivek Balasaheb Sabale, Thirumalai M., Atul Kumar | Date: 2025-04-11
The classification of quantum states into distinct classes poses a significant challenge. In this study, we address this problem using quantum neural networks in combination with a problem-inspired circuit and customised as well as predefined ans\"{a}tz. To facilitate the resource-efficient quantum ... more
-4.5823 AdvBDGen: Adversarially Fortified Prompt-Specific Fuzzy Backdoor Generator Against LLM Alignment
Authors: Pankayaraj Pathmanathan, Udari Madhushani Sehwag, Michael-Andrei Panaitescu-Liess, Furong Huang | Date: 2025-04-11
With the growing adoption of reinforcement learning with human feedback (RLHF) for aligning large language models (LLMs), the risk of backdoor installation during alignment has increased, leading to unintended and harmful behaviors. Existing backdoor triggers are typically limited to fixed word patt ... more
-4.5826 SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints
Authors: Miruna Cretu, Charles Harris, Ilia Igashov, Arne Schneuing, Marwin Segler, Bruno Correia, Julien Roy, Emmanuel Bengio, Pietro Li\`o | Date: 2025-04-11
Generative models see increasing use in computer-aided drug design. However, while performing well at capturing distributions of molecular motifs, they often produce synthetically inaccessible molecules. To address this, we introduce SynFlowNet, a GFlowNet model whose action space uses chemical reac ... more
-4.5887 RNN-Transducer-based Losses for Speech Recognition on Noisy Targets
Authors: Vladimir Bataev | Date: 2025-04-11
Training speech recognition systems on noisy transcripts is a significant challenge in industrial pipelines, where datasets are enormous and ensuring accurate transcription for every instance is difficult. In this work, we introduce novel loss functions to mitigate the impact of transcription errors ... more
-4.5926 PolygonGNN: Representation Learning for Polygonal Geometries with Heterogeneous Visibility Graph
Authors: Dazhou Yu, Yuntong Hu, Yun Li, Liang Zhao | Date: 2025-04-11
Polygon representation learning is essential for diverse applications, encompassing tasks such as shape coding, building pattern classification, and geographic question answering. While recent years have seen considerable advancements in this field, much of the focus has been on single polygons, ove ... more
-4.5957 Parametric Reachable Sets Via Controlled Dynamical Embeddings
Authors: Akash Harapanahalli, Samuel Coogan | Date: 2025-04-11
In this work, we propose a new framework for reachable set computation through continuous evolution of a set of parameters and offsets which define a parametope, through the intersection of constraints. This results in a dynamical approach towards nonlinear reachability analysis: a single trajectory ... more
-4.5972 Prompt-Enabled Large AI Models for CSI Feedback
Authors: Jiajia Guo, Yiming Cui, Chao-Kai Wen, Shi Jin | Date: 2025-04-11
Artificial intelligence (AI) has emerged as a promising tool for channel state information (CSI) feedback. While recent research primarily focuses on improving feedback accuracy on a specific dataset through novel architectures, the underlying mechanism of AI-based CSI feedback remains unclear. This ... more
-4.5979 Effective Method for Inverse Ising Problem under Missing Observations in Restricted Boltzmann Machines
Authors: Kaiji Sekimoto, Muneki Yasuda | Date: 2025-04-11
Restricted Boltzmann machines (RBMs) are energy-based models analogous to the Ising model and are widely applied in statistical machine learning. The standard inverse Ising problem with a complete dataset requires computing both data and model expectations and is computationally challenging because ... more
-4.5981 Diversity-aware Dual-promotion Poisoning Attack on Sequential Recommendation
Authors: Yuchuan Zhao, Tong Chen, Junliang Yu, Kai Zheng, Lizhen Cui, Hongzhi Yin | Date: 2025-04-11
Sequential recommender systems (SRSs) excel in capturing users' dynamic interests, thus playing a key role in various industrial applications. The popularity of SRSs has also driven emerging research on their security aspects, where data poisoning attack for targeted item promotion is a typical exam ... more
-4.6085 Future Sight and Tough Fights: Revolutionizing Sequential Recommendation with FENRec
Authors: Yu-Hsuan Huang, Ling Lo, Hongxia Xie, Hong-Han Shuai, Wen-Huang Cheng | Date: 2025-04-11
Sequential recommendation (SR) systems predict user preferences by analyzing time-ordered interaction sequences. A common challenge for SR is data sparsity, as users typically interact with only a limited number of items. While contrastive learning has been employed in previous approaches to address ... more
-4.6102 Human and LLM Biases in Hate Speech Annotations: A Socio-Demographic Analysis of Annotators and Targets
Authors: Tommaso Giorgi, Lorenzo Cima, Tiziano Fagni, Marco Avvenuti, Stefano Cresci | Date: 2025-04-11
The rise of online platforms exacerbated the spread of hate speech, demanding scalable and effective detection. However, the accuracy of hate speech detection systems heavily relies on human-labeled data, which is inherently susceptible to biases. While previous work has examined the issue, the inte ... more
-4.6104 MemoRAG: Boosting Long Context Processing with Global Memory-Enhanced Retrieval Augmentation
Authors: Hongjin Qian, Zheng Liu, Peitian Zhang, Kelong Mao, Defu Lian, Zhicheng Dou, Tiejun Huang | Date: 2025-04-11
Processing long contexts presents a significant challenge for large language models (LLMs). While recent advancements allow LLMs to handle much longer contexts than before (e.g., 32K or 128K tokens), it is computationally expensive and can still be insufficient for many applications. Retrieval-Augme ... more
RAG
-4.6116 Sublinear Regret for a Class of Continuous-Time Linear-Quadratic Reinforcement Learning Problems
Authors: Yilie Huang, Yanwei Jia, Xun Yu Zhou | Date: 2025-04-11
We study reinforcement learning (RL) for a class of continuous-time linear-quadratic (LQ) control problems for diffusions, where states are scalar-valued and running control rewards are absent but volatilities of the state processes depend on both state and control variables. We apply a model-free a ... more
-4.6251 DMol: A Schedule-Driven Diffusion Model for Highly Efficient and Versatile Molecule Generation
Authors: Peizhi Niu, Yu-Hsiang Wang, Vishal Rana, Chetan Rupakheti, Abhishek Pandey, Olgica Milenkovic | Date: 2025-04-11
We introduce a new graph diffusion model for small molecule generation, \emph{DMol}, which outperforms the state-of-the-art DiGress model in terms of validity by roughly $1.5\%$ across all benchmarking datasets while reducing the number of diffusion steps by at least $10$-fold, and the running time ... more
-4.6253 Review, Definition and Challenges of Electrical Energy Hubs
Authors: Giacomo Bastianel, Jan Kircheis, Merijn Van Deyck, Dongyeong Lee, Geraint Chaffey, Marta Vanin, Hakan Ergun, Jef Beerten, Dirk Van Hertem | Date: 2025-04-11
To transition towards a carbon-neutral power system, considerable amounts of renewable energy generation capacity are being installed in the North Sea area. Consequently, projects aggregating many gigawatts of power generation capacity and transmitting renewable energy to the main load centers are b ... more
-4.6276 Plastic tensor networks for interpretable generative modeling
Authors: Katsuya O. Akamatsu, Kenji Harada, Tsuyoshi Okubo, Naoki Kawashima | Date: 2025-04-11
A structural optimization scheme for a single-layer nonnegative adaptive tensor tree (NATT) that models a target probability distribution is proposed. The NATT scheme, by construction, has the advantage that it is interpretable as a probabilistic graphical model. We consider the NATT scheme and a re ... more
-4.6277 DLF: Disentangled-Language-Focused Multimodal Sentiment Analysis
Authors: Pan Wang, Qiang Zhou, Yawen Wu, Tianlong Chen, Jingtong Hu | Date: 2025-04-11
Multimodal Sentiment Analysis (MSA) leverages heterogeneous modalities, such as language, vision, and audio, to enhance the understanding of human sentiment. While existing models often focus on extracting shared information across modalities or directly fusing heterogeneous modalities, such approac ... more
-4.6287 Societal Impacts Research Requires Benchmarks for Creative Composition Tasks
Authors: Judy Hanwen Shen, Carlos Guestrin | Date: 2025-04-11
Foundation models that are capable of automating cognitive tasks represent a pivotal technological shift, yet their societal implications remain unclear. These systems promise exciting advances, yet they also risk flooding our information ecosystem with formulaic, homogeneous, and potentially mislea ... more
-4.6305 A spatial hypergraph model to smoothly interpolate between pairwise graphs and hypergraphs to study higher-order structures
Authors: Omar Eldaghar, Yu Zhu, David F. Gleich | Date: 2025-04-11
We introduce a spatial graph and hypergraph model that smoothly interpolates between a graph with purely pairwise edges and a graph where all connections are in large hyperedges. The key component is a spatial clustering resolution parameter that varies between assigning all the vertices in a spatia ... more
-4.6322 Navigating the Rabbit Hole: Emergent Biases in LLM-Generated Attack Narratives Targeting Mental Health Groups
Authors: Rijul Magu, Arka Dutta, Sean Kim, Ashiqur R. KhudaBukhsh, Munmun De Choudhury | Date: 2025-04-11
Large Language Models (LLMs) have been shown to demonstrate imbalanced biases against certain groups. However, the study of unprovoked targeted attacks by LLMs towards at-risk populations remains underexplored. Our paper presents three novel contributions: (1) the explicit evaluation of LLM-generate ... more
-4.6349 Error estimate for regularized optimal transport problems via Bregman divergence
Authors: Keiichi Morikuni, Koya Sakakibara, Asuka Takatsu | Date: 2025-04-11
Regularization by the Shannon entropy enables us to efficiently and approximately solve optimal transport problems on a finite set. This paper is concerned with regularized optimal transport problems via Bregman divergence. We introduce the required properties for Bregman divergences, provide a non- ... more
-4.6394 D-Feat Occlusions: Diffusion Features for Robustness to Partial Visual Occlusions in Object Recognition
Authors: Rupayan Mallick, Sibo Dong, Nataniel Ruiz, Sarah Adel Bargal | Date: 2025-04-11
Applications of diffusion models for visual tasks have been quite noteworthy. This paper targets making classification models more robust to occlusions for the task of object recognition by proposing a pipeline that utilizes a frozen diffusion model. Diffusion features have demonstrated success in i ... more
-4.6452 Visualisation of a multidimensional point cloud as a 3D swarm of avatars
Authors: Leszek Luchowski, Dariusz Pojda | Date: 2025-04-11
The article presents an innovative approach to the visualisation of multidimensional data, using icons inspired by Chernoff faces. The approach merges classical projection techniques with the assignment of particular data dimensions to mimic features, capitalizing on the natural ability of the human ... more
-4.6527 Learning Equivariant Non-Local Electron Density Functionals
Authors: Nicholas Gao, Eike Eberhard, Stephan G\"unnemann | Date: 2025-04-11
The accuracy of density functional theory hinges on the approximation of non-local contributions to the exchange-correlation (XC) functional. To date, machine-learned and human-designed approximations suffer from insufficient accuracy, limited scalability, or dependence on costly reference data. To ... more
-4.6626 Beware of "Explanations" of AI
Authors: David Martens, Galit Shmueli, Theodoros Evgeniou, Kevin Bauer, Christian Janiesch, Stefan Feuerriegel, Sebastian Gabel, Sofie Goethals, Travis Greene, Nadja Klein, Mathias Kraus, Niklas K\"uhl, Claudia Perlich, Wouter Verbeke, Alona Zharova, Patrick Zschech, Foster Provost | Date: 2025-04-11
Understanding the decisions made and actions taken by increasingly complex AI system remains a key challenge. This has led to an expanding field of research in explainable artificial intelligence (XAI), highlighting the potential of explanations to enhance trust, support adoption, and meet regulator ... more
-4.6644 MARS: Memory-Enhanced Agents with Reflective Self-improvement
Authors: Xuechen Liang, Meiling Tao, Yinghui Xia, Jianhui Wang, Kun Li, Yijin Wang, Jingsong Yang, Tianyu Shi, Yuantao Wang, Miao Zhang, Xueqian Wang | Date: 2025-04-11
Large language models (LLMs) have made significant advances in the field of natural language processing, but they still face challenges such as continuous decision-making, lack of long-term memory, and limited context windows in dynamic environments. To address these issues, this paper proposes an i ... more
-4.669 Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation
Authors: Yuhui Zhang, Yuchang Su, Yiming Liu, Xiaohan Wang, James Burgess, Elaine Sui, Chenyu Wang, Josiah Aklilu, Alejandro Lozano, Anjiang Wei, Ludwig Schmidt, Serena Yeung-Levy | Date: 2025-04-11
The rapid development of vision language models (VLMs) demands rigorous and reliable evaluation. However, current visual question answering (VQA) benchmarks often depend on open-ended questions, making accurate evaluation difficult due to the variability in natural language responses. To address thi ... more
-4.6769 Masked Scene Modeling: Narrowing the Gap Between Supervised and Self-Supervised Learning in 3D Scene Understanding
Authors: Pedro Hermosilla, Christian Stippel, Leon Sick | Date: 2025-04-11
Self-supervised learning has transformed 2D computer vision by enabling models trained on large, unannotated datasets to provide versatile off-the-shelf features that perform similarly to models trained with labels. However, in 3D scene understanding, self-supervised methods are typically only used ... more
-4.6805 ODEStream: A Buffer-Free Online Learning Framework with ODE-based Adaptor for Streaming Time Series Forecasting
Authors: Futoon M. Abushaqra, Hao Xue, Yongli Ren, Flora D. Salim | Date: 2025-04-11
Addressing the challenges of irregularity and concept drift in streaming time series is crucial for real-world predictive modelling. Previous studies in time series continual learning often propose models that require buffering long sequences, potentially restricting the responsiveness of the infere ... more
-4.6815 SVG-IR: Spatially-Varying Gaussian Splatting for Inverse Rendering
Authors: Hanxiao Sun, YuPeng Gao, Jin Xie, Jian Yang, Beibei Wang | Date: 2025-04-11
Reconstructing 3D assets from images, known as inverse rendering (IR), remains a challenging task due to its ill-posed nature. 3D Gaussian Splatting (3DGS) has demonstrated impressive capabilities for novel view synthesis (NVS) tasks. Methods apply it to relighting by separating radiance into BRDF p ... more
-4.687 S-EO: A Large-Scale Dataset for Geometry-Aware Shadow Detection in Remote Sensing Applications
Authors: Masquil El\'ias, Mar\'i Roger, Ehret Thibaud, Meinhardt-Llopis Enric, Mus\'e Pablo, Facciolo Gabriele | Date: 2025-04-11
We introduce the S-EO dataset: a large-scale, high-resolution dataset, designed to advance geometry-aware shadow detection. Collected from diverse public-domain sources, including challenge datasets and government providers such as USGS, our dataset comprises 702 georeferenced tiles across the USA, ... more
-4.6924 Efficient Sparse Flow Decomposition Methods for RNA Multi-Assembly
Authors: Mathieu Besan\c{c}on | Date: 2025-04-11
Decomposing a flow on a Directed Acyclic Graph (DAG) into a weighted sum of a small number of paths is an essential task in operations research and bioinformatics. This problem, referred to as Sparse Flow Decomposition (SFD), has gained significant interest, in particular for its application in RNA ... more
-4.6997 Bio2Token: All-atom tokenization of any biomolecular structure with Mamba
Authors: Andrew Liu, Axel Elaldi, Nathan Russell, Olivia Viessmann | Date: 2025-04-11
Efficient encoding and representation of large 3D molecular structures with high fidelity is critical for biomolecular design applications. Despite this, many representation learning approaches restrict themselves to modeling smaller systems or use coarse-grained approximations of the systems, for e ... more
-4.7003 Dissipative iFIR filters for data-driven design
Authors: Zixing Wang, Yi Zhang, Fulvio Forni | Date: 2025-04-11
We tackle the problem of providing closed-loop stability guarantees with a scalable data-driven design. We combine virtual reference feedback tuning with dissipativity constraints on the controller for closed-loop stability. The constraints are formulated as a set of linear inequalities in the frequ ... more
-4.7003 Cohesive Subgraph Discovery in Hypergraphs: A Locality-Driven Indexing Framework
Authors: Song Kim, Dahee Kim, Taejoon Han, Junghoon Kim, Hyun Ji Jeong, Jungeun Kim | Date: 2025-04-11
Hypergraphs, increasingly utilised for modelling complex and diverse relationships in modern networks, gain much attention representing intricate higher-order interactions. Among various challenges, cohesive subgraph discovery is one of the fundamental problems and offers deep insights into these st ... more
-4.702 Differentially Private Joint Independence Test
Authors: Xingwei Liu, Yuexin Chen, Wangli Xu | Date: 2025-04-11
Identification of joint dependence among more than two random vectors plays an important role in many statistical applications, where the data may contain sensitive or confidential information. In this paper, we consider the the $d$-variable Hilbert-Schmidt independence criterion (dHSIC) in the cont ... more
-4.7021 PEEL the Layers and Find Yourself: Revisiting Inference-time Data Leakage for Residual Neural Networks
Authors: Huzaifa Arif, Keerthiram Murugesan, Payel Das, Alex Gittens, Pin-Yu Chen | Date: 2025-04-11
This paper explores inference-time data leakage risks of deep neural networks (NNs), where a curious and honest model service provider is interested in retrieving users' private data inputs solely based on the model inference results. Particularly, we revisit residual NNs due to their popularity in ... more
-4.7056 Towards a Higher Roofline for Matrix-Vector Multiplication in Matrix-Free HOSFEM
Authors: Zijian Cao, Qiao Sun, Tiangong Zhang, Huiyuan Li | Date: 2025-04-11
The high-order/spectral finite element method (HOSFEM) is a widely used numerical method for solving PDEs, with its performance primarily relying on axhelm, a matrix-free kernel for element-local matrix-vector multiplications. In axhelm, geometric factors account for over half of memory access but m ... more
-4.7079 Releasing Differentially Private Event Logs Using Generative Models
Authors: Frederik Wangelik, Majid Rafiei, Mahsa Pourbafrani, Wil M. P. van der Aalst | Date: 2025-04-11
In recent years, the industry has been witnessing an extended usage of process mining and automated event data analysis. Consequently, there is a rising significance in addressing privacy apprehensions related to the inclusion of sensitive and private information within event data utilized by proces ... more
-4.7118 Controlling a Social Network of Individuals with Coevolving Actions and Opinions
Authors: Roberta Raineri, Giacomo Como, Fabio Fagnani, Mengbin Ye, Lorenzo Zino | Date: 2025-04-11
In this paper, we consider a population of individuals who have actions and opinions, which coevolve, mutually influencing one another on a complex network structure. In particular, we formulate a control problem for this social network, in which we assume that we can inject into the network a commi ... more
-4.713 NeuRadar: Neural Radiance Fields for Automotive Radar Point Clouds
Authors: Mahan Rafidashti, Ji Lan, Maryam Fatemi, Junsheng Fu, Lars Hammarstrand, Lennart Svensson | Date: 2025-04-11
Radar is an important sensor for autonomous driving (AD) systems due to its robustness to adverse weather and different lighting conditions. Novel view synthesis using neural radiance fields (NeRFs) has recently received considerable attention in AD due to its potential to enable efficient testing a ... more
-4.7231 PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models
Authors: Fanxu Meng, Zhaohui Wang, Muhan Zhang | Date: 2025-04-11
To parameter-efficiently fine-tune (PEFT) large language models (LLMs), the low-rank adaptation (LoRA) method approximates the model changes $\Delta W \in \mathbb{R}^{m \times n}$ through the product of two matrices $A \in \mathbb{R}^{m \times r}$ and $B \in \mathbb{R}^{r \times n}$, where $r \ll \m ... more
-4.7328 VideoPainter: Any-length Video Inpainting and Editing with Plug-and-Play Context Control
Authors: Yuxuan Bian, Zhaoyang Zhang, Xuan Ju, Mingdeng Cao, Liangbin Xie, Ying Shan, Qiang Xu | Date: 2025-04-11
Video inpainting, which aims to restore corrupted video content, has experienced substantial progress. Despite these advances, existing methods, whether propagating unmasked region pixels through optical flow and receptive field priors, or extending image-inpainting models temporally, face challenge ... more
-4.7493 A Survey on Mixture of Experts in Large Language Models
Authors: Weilin Cai, Juyong Jiang, Fan Wang, Jing Tang, Sunghun Kim, Jiayi Huang | Date: 2025-04-11
Large language models (LLMs) have garnered unprecedented advancements across diverse fields, ranging from natural language processing to computer vision and beyond. The prowess of LLMs is underpinned by their substantial model size, extensive and diverse datasets, and the vast computational power ha ... more
LLMs
-4.7512 NLP Security and Ethics, in the Wild
Authors: Heather Lent, Erick Galinkin, Yiyi Chen, Jens Myrup Pedersen, Leon Derczynski, Johannes Bjerva | Date: 2025-04-11
As NLP models are used by a growing number of end-users, an area of increasing importance is NLP Security (NLPSec): assessing the vulnerability of models to malicious attacks and developing comprehensive countermeasures against them. While work at the intersection of NLP and cybersecurity has the po ... more
-4.754 Enhancing Job Salary Prediction with Disentangled Composition Effect Modeling: A Neural Prototyping Approach
Authors: Yang Ji, Ying Sun, Hengshu Zhu | Date: 2025-04-11
In the era of the knowledge economy, understanding how job skills influence salary is crucial for promoting recruitment with competitive salary systems and aligned salary expectations. Despite efforts on salary prediction based on job positions and talent demographics, there still lacks methods to e ... more
-4.7568 Domain Generalization via Discrete Codebook Learning
Authors: Shaocong Long, Qianyu Zhou, Xikun Jiang, Chenhao Ying, Lizhuang Ma, Yuan Luo | Date: 2025-04-11
Domain generalization (DG) strives to address distribution shifts across diverse environments to enhance model's generalizability. Current DG approaches are confined to acquiring robust representations with continuous features, specifically training at the pixel level. However, this DG paradigm may ... more
-4.7584 "Sorry for bugging you so much." Exploring Developers' Behavior Towards Privacy-Compliant Implementation
Authors: Stefan Albert Horstmann, Sandy Hong, David Klein, Raphael Serafini, Martin Degeling, Martin Johns, Veelasha Moonsamy, Alena Naiakshina | Date: 2025-04-11
While protecting user data is essential, software developers often fail to fulfill privacy requirements. However, the reasons why they struggle with privacy-compliant implementation remain unclear. Is it due to a lack of knowledge, or is it because of insufficient support? To provide foundational in ... more
-4.7601 FANeRV: Frequency Separation and Augmentation based Neural Representation for Video
Authors: Li Yu, Zhihui Li, Jimin Xiao, Moncef Gabbouj | Date: 2025-04-11
Neural representations for video (NeRV) have gained considerable attention for their strong performance across various video tasks. However, existing NeRV methods often struggle to capture fine spatial details, resulting in vague reconstructions. In this paper, we present a Frequency Separation and ... more
-4.7658 Understanding the Cluster LP for Correlation Clustering
Authors: Nairen Cao, Vincent Cohen-Addad, Euiwoong Lee, Shi Li, Alantha Newman, Lukas Vogl | Date: 2025-04-11
In the classic Correlation Clustering problem introduced by Bansal, Blum, and Chawla~(FOCS 2002), the input is a complete graph where edges are labeled either $+$ or $-$, and the goal is to find a partition of the vertices that minimizes the sum of the +edges across parts plus the sum of the -edges ... more
-4.7668 Task-Parameter Nexus: Task-Specific Parameter Learning for Model-Based Control
Authors: Sheng Cheng, Ran Tao, Yuliang Gu, Shenlong Wang, Xiaofeng Wang, Naira Hovakimyan | Date: 2025-04-11
This paper presents the Task-Parameter Nexus (TPN), a learning-based approach for online determination of the (near-)optimal control parameters of model-based controllers (MBCs) for tracking tasks. In TPN, a deep neural network is introduced to predict the control parameters for any given tracking t ... more
-4.7839 Investigating Adversarial Trigger Transfer in Large Language Models
Authors: Nicholas Meade, Arkil Patel, Siva Reddy | Date: 2025-04-11
Recent work has developed optimization procedures to find token sequences, called adversarial triggers, which can elicit unsafe responses from aligned language models. These triggers are believed to be highly transferable, i.e., a trigger optimized on one model can jailbreak other models. In this pa ... more
-4.7935 FamilyTool: A Multi-hop Personalized Tool Use Benchmark
Authors: Yuxin Wang, Yiran Guo, Yining Zheng, Zhangyue Yin, Shuo Chen, Jie Yang, Jiajun Chen, Xuanjing Huang, Xipeng Qiu | Date: 2025-04-11
The integration of tool learning with Large Language Models (LLMs) has expanded their capabilities in handling complex tasks by leveraging external tools. However, existing benchmarks for tool learning inadequately address critical real-world personalized scenarios, particularly those requiring mult ... more
-4.7939 Totally equimodular matrices: decomposition and triangulation
Authors: Patrick Chervet, Roland Grappe, Mathieu Vall\'ee | Date: 2025-04-11
Totally equimodular matrices generalize totally unimodular matrices and arise in the context of box-total dual integral polyhedra. This work further explores the parallels between these two classes and introduces foundational building blocks for constructing totally equimodular matrices. Consequentl ... more
-4.795 Caption Anything in Video: Fine-grained Object-centric Captioning via Spatiotemporal Multimodal Prompting
Authors: Yunlong Tang, Jing Bi, Chao Huang, Susan Liang, Daiki Shimada, Hang Hua, Yunzhong Xiao, Yizhi Song, Pinxin Liu, Mingqian Feng, Junjia Guo, Zhuo Liu, Luchuan Song, Ali Vosoughi, Jinxi He, Liu He, Zeliang Zhang, Jiebo Luo, Chenliang Xu | Date: 2025-04-11
We present CAT-V (Caption AnyThing in Video), a training-free framework for fine-grained object-centric video captioning that enables detailed descriptions of user-selected objects through time. CAT-V integrates three key components: a Segmenter based on SAMURAI for precise object segmentation acros ... more
-4.804 Missing Premise exacerbates Overthinking: Are Reasoning Models losing Critical Thinking Skill?
Authors: Chenrui Fan, Ming Li, Lichao Sun, Tianyi Zhou | Date: 2025-04-11
We find that the response length of reasoning LLMs, whether trained by reinforcement learning or supervised learning, drastically increases for ill-posed questions with missing premises (MiP), ending up with redundant and ineffective thinking. This newly introduced scenario exacerbates the general o ... more
-4.8056 Randomized Pairwise Learning with Adaptive Sampling: A PAC-Bayes Analysis
Authors: Sijia Zhou, Yunwen Lei, Ata Kab\'an | Date: 2025-04-11
We study stochastic optimization with data-adaptive sampling schemes to train pairwise learning models. Pairwise learning is ubiquitous, and it covers several popular learning tasks such as ranking, metric learning and AUC maximization. A notable difference of pairwise learning from pointwise learni ... more
-4.8085 Unsolvable Problem Detection: Robust Understanding Evaluation for Large Multimodal Models
Authors: Atsuyuki Miyai, Jingkang Yang, Jingyang Zhang, Yifei Ming, Qing Yu, Go Irie, Yixuan Li, Hai Li, Ziwei Liu, Kiyoharu Aizawa | Date: 2025-04-11
This paper introduces a novel task to evaluate the robust understanding capability of Large Multimodal Models (LMMs), termed $\textbf{Unsolvable Problem Detection (UPD)}$. Multiple-choice question answering (MCQA) is widely used to assess the understanding capability of LMMs, but it does not guarant ... more
-4.8103 Mass Balance Approximation of Unfolding Improves Potential-Like Methods for Protein Stability Predictions
Authors: Ivan Rossi, Guido Barducci, Tiziana Sanavia, Paola Turina, Emidio Capriotti, Piero Fariselli | Date: 2025-04-11
The prediction of protein stability changes following single-point mutations plays a pivotal role in computational biology, particularly in areas like drug discovery, enzyme reengineering, and genetic disease analysis. Although deep-learning strategies have pushed the field forward, their use in sta ... more
-4.8108 Robust Fusion Controller: Degradation-aware Image Fusion with Fine-grained Language Instructions
Authors: Hao Zhang, Yanping Zha, Qingwei Zhuang, Zhenfeng Shao, Jiayi Ma | Date: 2025-04-11
Current image fusion methods struggle to adapt to real-world environments encompassing diverse degradations with spatially varying characteristics. To address this challenge, we propose a robust fusion controller (RFC) capable of achieving degradation-aware image fusion through fine-grained language ... more
-4.8111 SIGMAN:Scaling 3D Human Gaussian Generation with Millions of Assets
Authors: Yuhang Yang, Fengqi Liu, Yixing Lu, Qin Zhao, Pingyu Wu, Wei Zhai, Ran Yi, Yang Cao, Lizhuang Ma, Zheng-Jun Zha, Junting Dong | Date: 2025-04-11
3D human digitization has long been a highly pursued yet challenging task. Existing methods aim to generate high-quality 3D digital humans from single or multiple views, but remain primarily constrained by current paradigms and the scarcity of 3D human assets. Specifically, recent approaches fall in ... more
-4.8123 Hogwild! Inference: Parallel LLM Generation via Concurrent Attention
Authors: Gleb Rodionov, Roman Garipov, Alina Shutova, George Yakushev, Vage Egiazarian, Anton Sinitsin, Denis Kuznedelev, Dan Alistarh | Date: 2025-04-11
Large Language Models (LLMs) have demonstrated the ability to tackle increasingly complex tasks through advanced reasoning, long-form content generation, and tool use. Solving these tasks often involves long inference-time computations. In human problem solving, a common strategy to expedite work is ... more
LLMs
-4.8126 VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement Fine-Tuning
Authors: Xinhao Li, Ziang Yan, Desen Meng, Lu Dong, Xiangyu Zeng, Yinan He, Yali Wang, Yu Qiao, Yi Wang, Limin Wang | Date: 2025-04-11
Recent advancements in reinforcement learning have significantly advanced the reasoning capabilities of multimodal large language models (MLLMs). While approaches such as Group Relative Policy Optimization (GRPO) and rule-based reward mechanisms demonstrate promise in text and image domains, their a ... more
-4.8205 Dataset Condensation for Recommendation
Authors: Jiahao Wu, Wenqi Fan, Jingfan Chen, Shengcai Liu, Qijiong Liu, Rui He, Qing Li, Ke Tang | Date: 2025-04-11
Training recommendation models on large datasets requires significant time and resources. It is desired to construct concise yet informative datasets for efficient training. Recent advances in dataset condensation show promise in addressing this problem by synthesizing small datasets. However, apply ... more
-4.8286 Boost Your Human Image Generation Model via Direct Preference Optimization
Authors: Sanghyeon Na, Yonggyu Kim, Hyunjoon Lee | Date: 2025-04-11
Human image generation is a key focus in image synthesis due to its broad applications, but even slight inaccuracies in anatomy, pose, or details can compromise realism. To address these challenges, we explore Direct Preference Optimization (DPO), which trains models to generate preferred (winning) ... more
-4.8315 Collision avoidance from monocular vision trained with novel view synthesis
Authors: Valentin Tordjman--Levavasseur (WILLOW), St\'ephane Caron (WILLOW) | Date: 2025-04-11
Collision avoidance can be checked in explicit environment models such as elevation maps or occupancy grids, yet integrating such models with a locomotion policy requires accurate state estimation. In this work, we consider the question of collision avoidance from an implicit environment model. We u ... more
-4.8356 SEE: Continual Fine-tuning with Sequential Ensemble of Experts
Authors: Zhilin Wang, Yafu Li, Xiaoye Qu, Yu Cheng | Date: 2025-04-11
Continual fine-tuning of large language models (LLMs) suffers from catastrophic forgetting. Rehearsal-based methods mitigate this problem by retaining a small set of old data. Nevertheless, they still suffer inevitable performance loss. Although training separate experts for each task can help preve ... more
-4.8395 Compressing 3D Gaussian Splatting by Noise-Substituted Vector Quantization
Authors: Haishan Wang, Mohammad Hassan Vali, Arno Solin | Date: 2025-04-11
3D Gaussian Splatting (3DGS) has demonstrated remarkable effectiveness in 3D reconstruction, achieving high-quality results with real-time radiance field rendering. However, a key challenge is the substantial storage cost: reconstructing a single scene typically requires millions of Gaussian splats, ... more
-4.8405 Distribution Grids May Be a Barrier To Residential Electrification
Authors: Priyadarshan, Constance Crozier, Kyri Baker, Kevin Kircher | Date: 2025-04-11
Replacing fossil-fueled appliances and vehicles with electric alternatives can reduce greenhouse gas emissions and air pollution in many settings. However, residential electrification can also raise electricity demand beyond the safe limits of electrical infrastructure. This can increase the risk of ... more
-4.8418 LeanGaussian: Breaking Pixel or Point Cloud Correspondence in Modeling 3D Gaussians
Authors: Jiamin Wu, Kenkun Liu, Han Gao, Xiaoke Jiang, Yao Yuan, Lei Zhang | Date: 2025-04-11
Recently, Gaussian splatting has demonstrated significant success in novel view synthesis. Current methods often regress Gaussians with pixel or point cloud correspondence, linking each Gaussian with a pixel or a 3D point. This leads to the redundancy of Gaussians being used to overfit the correspon ... more
-4.8486 DeCoMa: Detecting and Purifying Code Dataset Watermarks through Dual Channel Code Abstraction
Authors: Yuan Xiao, Yuchen Chen, Shiqing Ma, Haocheng Huang, Chunrong Fang, Yanwei Chen, Weisong Sun, Yunfeng Zhu, Xiaofang Zhang, Zhenyu Chen | Date: 2025-04-11
Watermarking is a technique to help identify the source of data points, which can be used to help prevent the misuse of protected datasets. Existing methods on code watermarking, leveraging the idea from the backdoor research, embed stealthy triggers as watermarks.Despite their high resilience again ... more
-4.8538 Bayesian Off-Policy Evaluation and Learning for Large Action Spaces
Authors: Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba | Date: 2025-04-11
In interactive systems, actions are often correlated, presenting an opportunity for more sample-efficient off-policy evaluation (OPE) and learning (OPL) in large action spaces. We introduce a unified Bayesian framework to capture these correlations through structured and informative priors. In this ... more
-4.8626 Model Equality Testing: Which Model Is This API Serving?
Authors: Irena Gao, Percy Liang, Carlos Guestrin | Date: 2025-04-11
Users often interact with large language models through black-box inference APIs, both for closed- and open-weight models (e.g., Llama models are popularly accessed via Amazon Bedrock and Azure AI Studio). In order to cut costs or add functionality, API providers may quantize, watermark, or finetune ... more
-4.8703 Distribution Shifts at Scale: Out-of-distribution Detection in Earth Observation
Authors: Burak Ekim, Girmaw Abebe Tadesse, Caleb Robinson, Gilles Hacheme, Michael Schmitt, Rahul Dodhia, Juan M. Lavista Ferres | Date: 2025-04-11
Training robust deep learning models is crucial in Earth Observation, where globally deployed models often face distribution shifts that degrade performance, especially in low-data regions. Out-of-distribution (OOD) detection addresses this by identifying inputs that deviate from in-distribution (ID ... more
-4.8746 Deep Neural Koopman Operator-based Economic Model Predictive Control of Shipboard Carbon Capture System
Authors: Minghao Han, Xunyuan Yin | Date: 2025-04-11
Shipboard carbon capture is a promising solution to help reduce carbon emissions in international shipping. In this work, we propose a data-driven dynamic modeling and economic predictive control approach within the Koopman framework. This integrated modeling and control approach is used to achieve ... more
-4.8814 Query Understanding in LLM-based Conversational Information Seeking
Authors: Yifei Yuan, Zahra Abbasiantaeb, Yang Deng, Mohammad Aliannejadi | Date: 2025-04-11
Query understanding in Conversational Information Seeking (CIS) involves accurately interpreting user intent through context-aware interactions. This includes resolving ambiguities, refining queries, and adapting to evolving information needs. Large Language Models (LLMs) enhance this process by int ... more
-4.8865 HalluciNot: Hallucination Detection Through Context and Common Knowledge Verification
Authors: Bibek Paudel, Alexander Lyzhov, Preetam Joshi, Puneet Anand | Date: 2025-04-11
This paper introduces a comprehensive system for detecting hallucinations in large language model (LLM) outputs in enterprise settings. We present a novel taxonomy of LLM responses specific to hallucination in enterprise applications, categorizing them into context-based, common knowledge, enterpris ... more
-4.9012 Aplicando diferencias finitas para resolver ecuaciones y sistemas de ecuaciones diferenciales parciales sobre dominios planos irregulares simplemente conexos y no conexos
Authors: Miriam Sosa-D\'iaz, Faustino Sanchez-Garduno | Date: 2025-04-11
Using exhaustion method and finite differences a new method to solve system of partial differential equations and is presented. This method allows design algorithm to solve linear and nonlinear systems in irregular domains. Applying this method to solve linear and nonlinear problems with prescribed ... more
-4.9021 A Meaningful Perturbation Metric for Evaluating Explainability Methods
Authors: Danielle Cohen, Hila Chefer, Lior Wolf | Date: 2025-04-11
Deep neural networks (DNNs) have demonstrated remarkable success, yet their wide adoption is often hindered by their opaque decision-making. To address this, attribution methods have been proposed to assign relevance values to each part of the input. However, different methods often produce entirely ... more
-4.9088 Domain-Conditioned Scene Graphs for State-Grounded Task Planning
Authors: Jonas Herzog, Jiangpin Liu, Yue Wang | Date: 2025-04-11
Recent robotic task planning frameworks have integrated large multimodal models (LMMs) such as GPT-4V. To address grounding issues of such models, it has been suggested to split the pipeline into perceptional state grounding and subsequent state-based planning. As we show in this work, the state gro ... more
-4.9119 Convergence of a continuous Galerkin method for the Biot-Allard poroelasticity system
Authors: Jakob S. Stokke, Markus Bause, Florin A. Radu | Date: 2025-04-11
We study a space-time finite element method for a system of poromechanics with memory effects that are modeled by a convolution integral. In the literature, the system is referred to as the Biot-Allard model. We recast the model as a first-order system in time, where the memory effects are transform ... more
-4.9125 On Coalgebraic Product Constructions for Markov Chains and Automata
Authors: Mayuko Kori, Kazuki Watanabe | Date: 2025-04-11
Verifying traces of systems is a central topic in formal verification. We study model checking of Markov chains (MCs) against temporal properties represented as (finite) automata. For instance, given an MC and a deterministic finite automaton (DFA), a simple but practically useful model checking pro ... more
-4.9211 CasTex: Cascaded Text-to-Texture Synthesis via Explicit Texture Maps and Physically-Based Shading
Authors: Mishan Aliev, Dmitry Baranchuk, Kirill Struminsky | Date: 2025-04-11
This work investigates text-to-texture synthesis using diffusion models to generate physically-based texture maps. We aim to achieve realistic model appearances under varying lighting conditions. A prominent solution for the task is score distillation sampling. It allows recovering a complex texture ... more
-4.9212 Towards Evidence-Based Tech Hiring Pipelines
Authors: Chris Brown, Swanand Vaishampayan | Date: 2025-04-11
Software engineers are responsible for developing, maintaining, and innovating software. To hire software engineers, organizations employ a tech hiring pipeline. This process typically consists of a series of steps to evaluate the extent to which applicants meet job requirements and can effectively ... more
-4.9233 Lifted Frequency-Domain Identification of Closed-Loop Multirate Systems: Applied to Dual-Stage Actuator Hard Disk Drives
Authors: Max van Haren, Masahiro Mae, Lennart Blanken, Tom Oomen | Date: 2025-04-11
Frequency-domain representations are crucial for the design and performance evaluation of controllers in multirate systems, specifically to address intersample performance. The aim of this paper is to develop an effective frequency-domain system identification technique for closed-loop multirate sys ... more
-4.9261 Holstein-Friesian Re-Identification using Multiple Cameras and Self-Supervision on a Working Farm
Authors: Phoenix Yu, Tilo Burghardt, Andrew W Dowsey, Neill W Campbell | Date: 2025-04-11
We present MultiCamCows2024, a farm-scale image dataset filmed across multiple cameras for the biometric identification of individual Holstein-Friesian cattle exploiting their unique black and white coat-patterns. Captured by three ceiling-mounted visual sensors covering adjacent barn areas over sev ... more
-4.9282 Physical spline for denoising object trajectory data by combining splines, ML feature regression and model knowledge
Authors: Jonas Torzewski | Date: 2025-04-11
This article presents a method for estimating the dynamic driving states (position, velocity, acceleration and heading) from noisy measurement data. The proposed approach is effective with both complete and partial observations, producing refined trajectory signals with kinematic consistency, ensuri ... more
-4.9285 cuTeSpMM: Accelerating Sparse-Dense Matrix Multiplication using GPU Tensor Cores
Authors: Lizhi Xiang, Omid Asudeh, Gerald Sabin, Aravind Sukumaran-Rajam, P. Sadayappan | Date: 2025-04-11
Many recent GPUs feature matrix multiplication engines (aka Tensor Core Units or TCUs) that perform small fixed-size matrix-matrix products at very high throughput. They have been used very effectively to speed up dense matrix-matrix multiplication libraries like Nvidia's cuBLAS, enabling significan ... more
-4.9296 Towards Efficient Roadside LiDAR Deployment: A Fast Surrogate Metric Based on Entropy-Guided Visibility
Authors: Yuze Jiang, Ehsan Javanmardi, Manabu Tsukada, Hiroshi Esaki | Date: 2025-04-11
The deployment of roadside LiDAR sensors plays a crucial role in the development of Cooperative Intelligent Transport Systems (C-ITS). However, the high cost of LiDAR sensors necessitates efficient placement strategies to maximize detection performance. Traditional roadside LiDAR deployment methods ... more
-4.93 Sampling from mixture distributions based on regime-switching diffusions
Authors: M. V. Tretyakov | Date: 2025-04-11
It is proposed to use stochastic differential equations with state-dependent switching rates (SDEwS) for sampling from finite mixture distributions. An Euler scheme with constant time step for SDEwS is considered. It is shown that the scheme converges with order one in weak sense and also in the erg ... more
-4.9304 RETROcode: Leveraging a Code Database for Improved Natural Language to Code Generation
Authors: Nathana\"el Beau, Beno\^it Crabb\'e | Date: 2025-04-11
As text and code resources have expanded, large-scale pre-trained models have shown promising capabilities in code generation tasks, typically employing supervised fine-tuning with problem statement-program pairs. However, increasing model size and data volume for performance gains also raises compu ... more
-4.9314 MM-STFlowNet: A Transportation Hub-Oriented Multi-Mode Passenger Flow Prediction Method via Spatial-Temporal Dynamic Graph Modeling
Authors: Ronghui Zhang, Wenbin Xing, Mengran Li, Zihan Wang, Junzhou Chen, Xiaolei Ma, Zhiyuan Liu, Zhengbing He | Date: 2025-04-11
Accurate and refined passenger flow prediction is essential for optimizing the collaborative management of multiple collection and distribution modes in large-scale transportation hubs. Traditional methods often focus only on the overall passenger volume, neglecting the interdependence between diffe ... more
-4.9318 Voting power in the Council of the European Union: A comprehensive sensitivity analysis
Authors: D\'ora Gr\'eta Petr\'oczy, L\'aszl\'o Csat\'o | Date: 2025-04-11
The Council of the European Union (EU) is one of the main decision-making bodies of the EU. A number of decisions require a qualified majority, the support of 55% of the member states (currently 15) that represent at least 65% of the total population. We investigate how the power distribution -- bas ... more
-4.936 DCSEG: Decoupled 3D Open-Set Segmentation using Gaussian Splatting
Authors: Luis Wiedmann, Luca Wiehe, David Rozenberszki | Date: 2025-04-11
Open-set 3D segmentation represents a major point of interest for multiple downstream robotics and augmented/virtual reality applications. We present a decoupled 3D segmentation pipeline to ensure modularity and adaptability to novel 3D representations as well as semantic segmentation foundation mod ... more
-4.94 Irregular Tensor Low-Rank Representation for Hyperspectral Image Representation
Authors: Bo Han, Yuheng Jia, Hui Liu, Junhui Hou | Date: 2025-04-11
Spectral variations pose a common challenge in analyzing hyperspectral images (HSI). To address this, low-rank tensor representation has emerged as a robust strategy, leveraging inherent correlations within HSI data. However, the spatial distribution of ground objects in HSIs is inherently irregular ... more
-4.9474 Mind the Gap: Evaluating Vision Systems in Small Data Applications
Authors: Samuel Stevens, S M Rayeed, Jenna Kline | Date: 2025-04-11
The practical application of AI tools for specific computer vision tasks relies on the "small-data regime" of hundreds to thousands of labeled samples. This small-data regime is vital for applications requiring expensive expert annotations, such as ecological monitoring, medical diagnostics or indus ... more
-4.9613 BBQRec: Behavior-Bind Quantization for Multi-Modal Sequential Recommendation
Authors: Kaiyuan Li, Rui Xiang, Yong Bai, Yongxiang Tang, Yanhua Cheng, Xialong Liu, Peng Jiang, Kun Gai | Date: 2025-04-11
Multi-modal sequential recommendation systems leverage auxiliary signals (e.g., text, images) to alleviate data sparsity in user-item interactions. While recent methods exploit large language models to encode modalities into discrete semantic IDs for autoregressive prediction, we identify two critic ... more
-4.9641 Glossy Object Reconstruction with Cost-effective Polarized Acquisition
Authors: Bojian Wu, Yifan Peng, Ruizhen Hu, Xiaowei Zhou | Date: 2025-04-11
The challenge of image-based 3D reconstruction for glossy objects lies in separating diffuse and specular components on glossy surfaces from captured images, a task complicated by the ambiguity in discerning lighting conditions and material properties using RGB data alone. While state-of-the-art met ... more
-4.9689 Toward Holistic Evaluation of Recommender Systems Powered by Generative Models
Authors: Yashar Deldjoo, Nikhil Mehta, Maheswaran Sathiamoorthy, Shuai Zhang, Pablo Castells, Julian McAuley | Date: 2025-04-11
Recommender systems powered by generative models (Gen-RecSys) extend beyond classical item ranking by producing open-ended content, which simultaneously unlocks richer user experiences and introduces new risks. On one hand, these systems can enhance personalization and appeal through dynamic explana ... more
-4.9717 Review of Case-Based Reasoning for LLM Agents: Theoretical Foundations, Architectural Components, and Cognitive Integration
Authors: Kostas Hatalis, Despina Christou, Vyshnavi Kondapalli | Date: 2025-04-11
Agents powered by Large Language Models (LLMs) have recently demonstrated impressive capabilities in various tasks. Still, they face limitations in tasks requiring specific, structured knowledge, flexibility, or accountable decision-making. While agents are capable of perceiving their environments, ... more
-4.9761 Towards Federated RLHF with Aggregated Client Preference for LLMs
Authors: Feijie Wu, Xiaoze Liu, Haoyu Wang, Xingchen Wang, Lu Su, Jing Gao | Date: 2025-04-11
Reinforcement learning with human feedback (RLHF) fine-tunes a pretrained large language model (LLM) using user preference data, enabling it to generate content aligned with human preferences. However, due to privacy concerns, users may be reluctant to share sensitive preference data. To address thi ... more
-4.9762 Different Paths, Same Destination: Designing New Physics-Inspired Dynamical Systems with Engineered Stability to Minimize the Ising Hamiltonian
Authors: E. M. H. E. B. Ekanayake, N. Shukla | Date: 2025-04-11
Oscillator Ising machines (OIMs) represent an exemplar case of using physics-inspired non-linear dynamical systems to solve computationally challenging combinatorial optimization problems (COPs). The computational performance of such systems is highly sensitive to the underlying dynamical properties ... more
-4.9768 Rotated Bitboards and Reinforcement Learning in Computer Chess and Beyond
Authors: Johannes Buchner | Date: 2025-04-11
There exist several techniques for representing the chess board inside the computer. In the first part of this paper, the concepts of the bitboard-representation and the advantages of (rotated) bitboards in move generation are explained. In order to illustrate those ideas practice, the concrete impl ... more
-4.9838 Uni-PrevPredMap: Extending PrevPredMap to a Unified Framework of Prior-Informed Modeling for Online Vectorized HD Map Construction
Authors: Nan Peng, Xun Zhou, Mingming Wang, Guisong Chen, Wenqi Xu | Date: 2025-04-11
Safety constitutes a foundational imperative for autonomous driving systems, necessitating the maximal incorporation of accessible external prior information. This study establishes that temporal perception buffers and cost-efficient maps inherently form complementary prior sources for online vector ... more
-4.9919 Amortized Bayesian Multilevel Models
Authors: Daniel Habermann, Marvin Schmitt, Lars K\"uhmichel, Andreas Bulling, Stefan T. Radev, Paul-Christian B\"urkner | Date: 2025-04-11
Multilevel models (MLMs) are a central building block of the Bayesian workflow. They enable joint, interpretable modeling of data across hierarchical levels and provide a fully probabilistic quantification of uncertainty. Despite their well-recognized advantages, MLMs pose significant computational ... more
-5.0034 PromptHMR: Promptable Human Mesh Recovery
Authors: Yufu Wang, Yu Sun, Priyanka Patel, Kostas Daniilidis, Michael J. Black, Muhammed Kocabas | Date: 2025-04-11
Human pose and shape (HPS) estimation presents challenges in diverse scenarios such as crowded scenes, person-person interactions, and single-view reconstruction. Existing approaches lack mechanisms to incorporate auxiliary "side information" that could enhance reconstruction accuracy in such challe ... more
-5.0077 Meta-LoRA: Meta-Learning LoRA Components for Domain-Aware ID Personalization
Authors: Bar{\i}\c{s} Batuhan Topal, Umut \"Ozyurt, Zafer Do\u{g}an Budak, Ramazan Gokberk Cinbis | Date: 2025-04-11
Recent advancements in text-to-image generative models, particularly latent diffusion models (LDMs), have demonstrated remarkable capabilities in synthesizing high-quality images from textual prompts. However, achieving identity personalization-ensuring that a model consistently generates subject-sp ... more
-5.0091 Low Rank Learning for Offline Query Optimization
Authors: Zixuan Yi, Yao Tian, Zachary G. Ives, Ryan Marcus | Date: 2025-04-11
Recent deployments of learned query optimizers use expensive neural networks and ad-hoc search policies. To address these issues, we introduce \textsc{LimeQO}, a framework for offline query optimization leveraging low-rank learning to efficiently explore alternative query plans with minimal resource ... more
-5.0091 FIORD: A Fisheye Indoor-Outdoor Dataset with LIDAR Ground Truth for 3D Scene Reconstruction and Benchmarking
Authors: Ulas Gunes, Matias Turkulainen, Xuqian Ren, Arno Solin, Juho Kannala, Esa Rahtu | Date: 2025-04-11
The development of large-scale 3D scene reconstruction and novel view synthesis methods mostly rely on datasets comprising perspective images with narrow fields of view (FoV). While effective for small-scale scenes, these datasets require large image sets and extensive structure-from-motion (SfM) pr ... more
-5.0183 Deep spatio-temporal point processes: Advances and new directions
Authors: Xiuyuan Cheng, Zheng Dong, Yao Xie | Date: 2025-04-11
Spatio-temporal point processes (STPPs) model discrete events distributed in time and space, with important applications in areas such as criminology, seismology, epidemiology, and social networks. Traditional models often rely on parametric kernels, limiting their ability to capture heterogeneous, ... more
-5.024 DUKAE: DUal-level Knowledge Accumulation and Ensemble for Pre-Trained Model-Based Continual Learning
Authors: Songze Li, Tonghua Su, Xu-Yao Zhang, Qixing Xu, Zhongjie Wang | Date: 2025-04-11
Pre-trained model-based continual learning (PTMCL) has garnered growing attention, as it enables more rapid acquisition of new knowledge by leveraging the extensive foundational understanding inherent in pre-trained model (PTM). Most existing PTMCL methods use Parameter-Efficient Fine-Tuning (PEFT) ... more
-5.0264 Beyond Tools: Generative AI as Epistemic Infrastructure in Education
Authors: Bodong Chen | Date: 2025-04-11
As generative AI rapidly integrates into educational infrastructures worldwide, it transforms how knowledge gets created, validated, and shared, yet current discourse inadequately addresses its implications as epistemic infrastructure mediating teaching and learning. This paper investigates how AI s ... more
-5.0265 CDER: Collaborative Evidence Retrieval for Document-level Relation Extraction
Authors: Khai Phan Tran, Xue Li | Date: 2025-04-11
Document-level Relation Extraction (DocRE) involves identifying relations between entities across multiple sentences in a document. Evidence sentences, crucial for precise entity pair relationships identification, enhance focus on essential text segments, improving DocRE performance. However, existi ... more
-5.0292 A Unified Agentic Framework for Evaluating Conditional Image Generation
Authors: Jifang Wang, Xue Yang, Longyue Wang, Zhenran Xu, Yiyu Wang, Yaowei Wang, Weihua Luo, Kaifu Zhang, Baotian Hu, Min Zhang | Date: 2025-04-11
Conditional image generation has gained significant attention for its ability to personalize content. However, the field faces challenges in developing task-agnostic, reliable, and explainable evaluation metrics. This paper introduces CIGEval, a unified agentic framework for comprehensive evaluation ... more
-5.0295 Can dialogues with AI systems help humans better discern visual misinformation?
Authors: Anku Rani, Valdemar Danry, Andy Lippman, Pattie Maes | Date: 2025-04-11
The widespread emergence of manipulated news media content poses significant challenges to online information integrity. This study investigates whether dialogues with AI about AI-generated images and associated news statements can increase human discernment abilities and foster short-term learning ... more
-5.0375 Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositions
Authors: Aaron Mishkin, Arda Sahiner, Mert Pilanci | Date: 2025-04-11
We develop fast algorithms and robust software for convex optimization of two-layer neural networks with ReLU activation functions. Our work leverages a convex reformulation of the standard weight-decay penalized training problem as a set of group-$\ell_1$-regularized data-local models, where locali ... more
-5.0377 Data Augmentation and Hyperparameter Tuning for Low-Resource MFA
Authors: Alessio Tosolini, Claire Bowern | Date: 2025-04-11
A continued issue for those working with computational tools and endangered and under-resourced languages is the lower accuracy of results for languages with smaller amounts of data. We attempt to ameliorate this issue by using data augmentation methods to increase corpus size, comparing augmentatio ... more
-5.0392 Large Scale Supervised Pretraining For Traumatic Brain Injury Segmentation
Authors: Constantin Ulrich, Tassilo Wald, Fabian Isensee, Klaus H. Maier-Hein | Date: 2025-04-11
The segmentation of lesions in Moderate to Severe Traumatic Brain Injury (msTBI) presents a significant challenge in neuroimaging due to the diverse characteristics of these lesions, which vary in size, shape, and distribution across brain regions and tissue types. This heterogeneity complicates tra ... more
-5.0399 Large-Scale (Semi-)Automated Security Assessment of Consumer IoT Devices -- A Roadmap
Authors: Pascal Sch\"ottle, Matthias Janetschek, Florian Merkle, Martin Nocker, Christoph Egger | Date: 2025-04-11
The Internet of Things (IoT) has rapidly expanded across various sectors, with consumer IoT devices - such as smart thermostats and security cameras - experiencing growth. Although these devices improve efficiency and promise additional comfort, they also introduce new security challenges. Common an ... more
-5.0427 MedSegFactory: Text-Guided Generation of Medical Image-Mask Pairs
Authors: Jiawei Mao, Yuhan Wang, Yucheng Tang, Daguang Xu, Kang Wang, Yang Yang, Zongwei Zhou, Yuyin Zhou | Date: 2025-04-11
This paper presents MedSegFactory, a versatile medical synthesis framework that generates high-quality paired medical images and segmentation masks across modalities and tasks. It aims to serve as an unlimited data repository, supplying image-mask pairs to enhance existing segmentation tools. The co ... more
-5.0497 Efficient Self-Supervised Learning for Earth Observation via Dynamic Dataset Curation
Authors: Thomas Kerdreux, Alexandre Tuel, Quentin Febvre, Alexis Mouche, Bertrand Chapron | Date: 2025-04-11
Self-supervised learning (SSL) has enabled the development of vision foundation models for Earth Observation (EO), demonstrating strong transferability across diverse remote sensing tasks. While prior work has focused on network architectures and training strategies, the role of dataset curation, es ... more
-5.0506 STaR: Seamless Spatial-Temporal Aware Motion Retargeting with Penetration and Consistency Constraints
Authors: Xiaohang Yang, Qing Wang, Jiahao Yang, Gregory Slabaugh, Shanxin Yuan | Date: 2025-04-11
Motion retargeting seeks to faithfully replicate the spatio-temporal motion characteristics of a source character onto a target character with a different body shape. Apart from motion semantics preservation, ensuring geometric plausibility and maintaining temporal consistency are also crucial for e ... more
-5.0533 BIFROST: Reinventing WiFi Signals Based on Dispersion Effect for Accurate Indoor Localization
Authors: Yimiao Sun, Yuan He, Jiacheng Zhang, Xin Na, Yande Chen, Weiguo Wang, Xiuzhen Guo | Date: 2025-04-11
WiFi-based device localization is a key enabling technology for smart applications, which has attracted numerous research studies in the past decade. Most of the existing approaches rely on Line-of-Sight (LoS) signals to work, while a critical problem is often neglected: In the real-world indoor env ... more
-5.0588 Joint Retrieval of Cloud properties using Attention-based Deep Learning Models
Authors: Zahid Hassan Tushar, Adeleke Ademakinwa, Jianwu Wang, Zhibo Zhang, Sanjay Purushotham | Date: 2025-04-11
Accurate cloud property retrieval is vital for understanding cloud behavior and its impact on climate, including applications in weather forecasting, climate modeling, and estimating Earth's radiation balance. The Independent Pixel Approximation (IPA), a widely used physics-based approach, simplifie ... more
-5.0617 Automated Business Process Analysis: An LLM-Based Approach to Value Assessment
Authors: William De Michele, Abel Armas Cervantes, Lea Frermann | Date: 2025-04-11
Business processes are fundamental to organizational operations, yet their optimization remains challenging due to the timeconsuming nature of manual process analysis. Our paper harnesses Large Language Models (LLMs) to automate value-added analysis, a qualitative process analysis technique that aim ... more
-5.0618 A Streamable Neural Audio Codec with Residual Scalar-Vector Quantization for Real-Time Communication
Authors: Xiao-Hang Jiang, Yang Ai, Rui-Chen Zheng, Zhen-Hua Ling | Date: 2025-04-11
This paper proposes StreamCodec, a streamable neural audio codec designed for real-time communication. StreamCodec adopts a fully causal, symmetric encoder-decoder structure and operates in the modified discrete cosine transform (MDCT) domain, aiming for low-latency inference and real-time efficient ... more
-5.0619 Information-Theoretic Reward Decomposition for Generalizable RLHF
Authors: Liyuan Mao, Haoran Xu, Amy Zhang, Weinan Zhang, Chenjia Bai | Date: 2025-04-11
A generalizable reward model is crucial in Reinforcement Learning from Human Feedback (RLHF) as it enables correctly evaluating unseen prompt-response pairs. However, existing reward models lack this ability, as they are typically trained by increasing the reward gap between chosen and rejected resp ... more
-5.0658 Optimizing LLM Queries in Relational Data Analytics Workloads
Authors: Shu Liu, Asim Biswal, Amog Kamsetty, Audrey Cheng, Luis Gaspar Schroeder, Liana Patel, Shiyi Cao, Xiangxi Mo, Ion Stoica, Joseph E. Gonzalez, Matei Zaharia | Date: 2025-04-11
Batch data analytics is a growing application for Large Language Models (LLMs). LLMs enable users to perform a wide range of natural language tasks, such as classification, entity extraction, and translation, over large datasets. However, LLM inference is highly costly and slow: for example, an NVID ... more
-5.0689 ShieldGemma 2: Robust and Tractable Image Content Moderation
Authors: Wenjun Zeng, Dana Kurniawan, Ryan Mullins, Yuchi Liu, Tamoghna Saha, Dirichi Ike-Njoku, Jindong Gu, Yiwen Song, Cai Xu, Jingjing Zhou, Aparna Joshi, Shravan Dheep, Mani Malek, Hamid Palangi, Joon Baek, Rick Pereira, Karthik Narasimhan | Date: 2025-04-11
We introduce ShieldGemma 2, a 4B parameter image content moderation model built on Gemma 3. This model provides robust safety risk predictions across the following key harm categories: Sexually Explicit, Violence \& Gore, and Dangerous Content for synthetic images (e.g. output of any image generatio ... more
-5.0714 Neural Network Enhanced Polyconvexification of Isotropic Energy Densities in Computational Mechanics
Authors: Lo\"ic Balazi, Timo Neumeier, Malte A. Peter, Daniel Peterseim | Date: 2025-04-11
We present a neural network approach for fast evaluation of parameter-dependent polyconvex envelopes, which are crucial in computational mechanics. Our method uses a neural network architecture that inherently encodes polyconvexity in the main variable by combining a feature extraction layer that co ... more
-5.0731 Detecting AI-generated Artwork
Authors: Meien Li, Mark Stamp | Date: 2025-04-11
The high efficiency and quality of artwork generated by Artificial Intelligence (AI) has created new concerns and challenges for human artists. In particular, recent improvements in generative AI have made it difficult for people to distinguish between human-generated and AI-generated art. In this r ... more
-5.074 Learning Occlusion-aware Decision-making from Agent Interaction via Active Perception
Authors: Jie Jia, Yiming Shu, Zhongxue Gan, Wenchao Ding | Date: 2025-04-11
Occlusion-aware decision-making is essential in autonomous driving due to the high uncertainty of various occlusions. Recent occlusion-aware decision-making methods encounter issues such as high computational complexity, scenario scalability challenges, or reliance on limited expert data. Benefiting ... more
-5.0801 A Digital Twin of an Electrical Distribution Grid: SoCal 28-Bus Dataset
Authors: Yiheng Xie, Lucien Werner, Kaibo Chen, Thuy-Linh Le, Christine Ortega, Steven Low | Date: 2025-04-11
We provide an open-access dataset of phasor & waveform measurement units (PMUs/WMUs) of a real-world electrical distribution network. The network consists of diverse sets of generation resources (including solar panels, fuel cells, natural gas generators, and utility interconnections), loads (includ ... more
-5.0844 nnLandmark: A Self-Configuring Method for 3D Medical Landmark Detection
Authors: Alexandra Ertl, Shuhan Xiao, Stefan Denner, Robin Peretzke, David Zimmerer, Peter Neher, Fabian Isensee, Klaus Maier-Hein | Date: 2025-04-11
Landmark detection plays a crucial role in medical imaging tasks that rely on precise spatial localization, including specific applications in diagnosis, treatment planning, image registration, and surgical navigation. However, manual annotation is labor-intensive and requires expert knowledge. Whil ... more
-5.0875 Bridging the Theoretical Gap in Randomized Smoothing
Authors: Blaise Delattre, Paul Caillon, Quentin Barth\'elemy, Erwan Fagnou, Alexandre Allauzen | Date: 2025-04-11
Randomized smoothing has become a leading approach for certifying adversarial robustness in machine learning models. However, a persistent gap remains between theoretical certified robustness and empirical robustness accuracy. This paper introduces a new framework that bridges this gap by leveraging ... more
-5.0884 Beyond Moore's Law: Harnessing the Redshift of Generative AI with Effective Hardware-Software Co-Design
Authors: Amir Yazdanbakhsh | Date: 2025-04-11
For decades, Moore's Law has served as a steadfast pillar in computer architecture and system design, promoting a clear abstraction between hardware and software. This traditional Moore's computing paradigm has deepened the rift between the two, enabling software developers to achieve near-exponenti ... more
-5.0909 Automated Fabrication of Magnetic Soft Microrobots
Authors: Kaitlyn Clancy, Siwen Xie, Griffin Smith, Onaizah Onaizah | Date: 2025-04-11
The advent of 3D printing has revolutionized many industries and has had similar improvements for soft robots. However, many challenges persist for these functional devices. Magnetic soft robots require the addition of magnetic particles that must be correctly oriented. There is a significant gap in ... more
-5.0914 SemiDAViL: Semi-supervised Domain Adaptation with Vision-Language Guidance for Semantic Segmentation
Authors: Hritam Basak, Zhaozheng Yin | Date: 2025-04-11
Domain Adaptation (DA) and Semi-supervised Learning (SSL) converge in Semi-supervised Domain Adaptation (SSDA), where the objective is to transfer knowledge from a source domain to a target domain using a combination of limited labeled target samples and abundant unlabeled target data. Although intu ... more
-5.0977 Physical configurations of a cell doublet with line tension, a theoretical study
Authors: Fabrice Delbary | Date: 2025-04-11
As a first approximation, early embryos may be modeled as foams whose shape depends on the surface tensions of each cell. However it has been remarked that exist line tensions at polarized exterior cellular interfaces (apical). In order to understand the changes it may imply on the usual foam model, ... more
-5.098 On a Characterization of Spartan Graphs
Authors: Neeldhara Misra, Saraswati Girish Nanoti | Date: 2025-04-11
The eternal vertex cover game is played between an attacker and a defender on an undirected graph $G$. The defender identifies $k$ vertices to position guards on to begin with. The attacker, on their turn, attacks an edge $e$, and the defender must move a guard along $e$ to defend the attack. The de ... more
-5.1013 Neural Signal Compression using RAMAN tinyML Accelerator for BCI Applications
Authors: Adithya Krishna, Sohan Debnath, Andr\'e van Schaik, Mahesh Mehendale, Chetan Singh Thakur | Date: 2025-04-11
High-quality, multi-channel neural recording is indispensable for neuroscience research and clinical applications. Large-scale brain recordings often produce vast amounts of data that must be wirelessly transmitted for subsequent offline analysis and decoding, especially in brain-computer interfaces ... more
-5.1013 Leveraging State Space Models in Long Range Genomics
Authors: Matvei Popov, Aymen Kallala, Anirudha Ramesh, Narimane Hennouni, Shivesh Khaitan, Rick Gentry, Alain-Sam Cohen | Date: 2025-04-11
Long-range dependencies are critical for understanding genomic structure and function, yet most conventional methods struggle with them. Widely adopted transformer-based models, while excelling at short-context tasks, are limited by the attention module's quadratic computational complexity and inabi ... more
-5.1021 Overcoming Dynamic Environments: A Hybrid Approach to Motion Planning for Manipulators
Authors: Ho Minh Quang Ngo, Dac Dang Khoa Nguyen, Dinh Tung Le, Gavin Paul | Date: 2025-04-11
Robotic manipulators operating in dynamic and uncertain environments require efficient motion planning to navigate obstacles while maintaining smooth trajectories. Velocity Potential Field (VPF) planners offer real-time adaptability but struggle with complex constraints and local minima, leading to ... more
-5.1022 RAMBO: RL-augmented Model-based Optimal Control for Whole-body Loco-manipulation
Authors: Jin Cheng, Dongho Kang, Gabriele Fadini, Guanya Shi, Stelian Coros | Date: 2025-04-11
Loco-manipulation -- coordinated locomotion and physical interaction with objects -- remains a major challenge for legged robots due to the need for both accurate force interaction and robustness to unmodeled dynamics. While model-based controllers provide interpretable dynamics-level planning and o ... more
-5.1044 Unifying Search and Recommendation: A Generative Paradigm Inspired by Information Theory
Authors: Jujia Zhao, Wenjie Wang, Chen Xu, Xiuying Wang, Zhaochun Ren, Suzan Verberne | Date: 2025-04-11
Recommender systems and search engines serve as foundational elements of online platforms, with the former delivering information proactively and the latter enabling users to seek information actively. Unifying both tasks in a shared model is promising since it can enhance user modeling and item und ... more
-5.1055 Deep Fair Learning: A Unified Framework for Fine-tuning Representations with Sufficient Networks
Authors: Enze Shi, Linglong Kong, Bei Jiang | Date: 2025-04-11
Ensuring fairness in machine learning is a critical and challenging task, as biased data representations often lead to unfair predictions. To address this, we propose Deep Fair Learning, a framework that integrates nonlinear sufficient dimension reduction with deep learning to construct fair and inf ... more
-5.1109 Learning Latent Hardening (LLH): Enhancing Deep Learning with Domain Knowledge for Material Inverse Problems
Authors: Qinyi Tian, Winston Lindqwister, Manolis Veveakis, Laura E. Dalton | Date: 2025-04-11
Advancements in deep learning and machine learning have improved the ability to model complex, nonlinear relationships, such as those encountered in complex material inverse problems. However, the effectiveness of these methods often depends on large datasets, which are not always available. In this ... more
-5.1112 Abstract Fractional Cauchy Problem: Existence of Propagators and Inhomogeneous Solution Representation
Authors: Dmytro Sytnyk, Barbara Wohlmuth | Date: 2025-04-11
We consider a Cauchy problem for the inhomogeneous differential equation given in terms of an unbounded linear operator $A$ and the Caputo fractional derivative of order $\alpha \in (0, 2)$ in time. The previously known representation of the mild solution to such a problem does not have a convention ... more
-5.1126 Enhancing Downstream Analysis in Genome Sequencing: Species Classification While Basecalling
Authors: Riselda Kodra, Hadjer Benmeziane, Irem Boybat, William Andrew Simon | Date: 2025-04-11
The ability to quickly and accurately identify microbial species in a sample, known as metagenomic profiling, is critical across various fields, from healthcare to environmental science. This paper introduces a novel method to profile signals coming from sequencing devices in parallel with determini ... more
-5.1132 On the Nature of Fractal Numbers and the Classical Continuum Hypothesis (CH)
Authors: Stanislav Semenov | Date: 2025-04-11
We propose a reinterpretation of the continuum grounded in the stratified structure of definability rather than classical cardinality. In this framework, a real number is not an abstract point on the number line, but an object expressible at some level Fn of a formal hierarchy. We introduce the noti ... more
-5.1146 Grouping Strategies on Two-Phase Methods for Bi-objective Combinatorial Optimization
Authors: Felipe O. Mota, Lu\'is Paquete, Daniel Vanderpooten | Date: 2025-04-11
Two-phase methods are commonly used to solve bi-objective combinatorial optimization problems. In the first phase, all extreme supported nondominated points are generated through a dichotomic search. This phase also allows the identification of search zones that may contain other nondominated points ... more
-5.1158 Regret Bounds for Robust Online Decision Making
Authors: Alexander Appel, Vanessa Kosoy | Date: 2025-04-11
We propose a framework which generalizes "decision making with structured observations" by allowing robust (i.e. multivalued) models. In this framework, each model associates each decision with a convex set of probability distributions over outcomes. Nature can choose distributions out of this set i ... more
-5.1164 Federated Neural Architecture Search with Model-Agnostic Meta Learning
Authors: Xinyuan Huang, Jiechao Gao | Date: 2025-04-11
Federated Learning (FL) often struggles with data heterogeneity due to the naturally uneven distribution of user data across devices. Federated Neural Architecture Search (NAS) enables collaborative search for optimal model architectures tailored to heterogeneous data to achieve higher accuracy. How ... more
Federated Learning
-5.1181 Optimality of Gradient-MUSIC for Spectral Estimation
Authors: Albert Fannjiang, Weilin Li, Wenjing Liao | Date: 2025-04-11
The goal of spectral estimation is to estimate the frequencies and amplitudes of a nonharmonic Fourier sum given noisy time samples. This paper introduces the Gradient-MUSIC algorithm, which is a novel nonconvex optimization reformulation of the classical MUSIC algorithm. Under the assumption that $ ... more
-5.1191 Efficient Simulation of Singularly Perturbed Systems Using a Stabilized Multirate Explicit Scheme
Authors: Yibo Shi, Cristian R. Rojas | Date: 2025-04-11
Singularly perturbed systems (SPSs) are prevalent in engineering applications, where numerically solving their initial value problems (IVPs) is challenging due to stiffness arising from multiple time scales. Classical explicit methods require impractically small time steps for stability, while impli ... more
-5.1209 Sort-free Gaussian Splatting via Weighted Sum Rendering
Authors: Qiqi Hou, Randall Rauwendaal, Zifeng Li, Hoang Le, Farzad Farhadzadeh, Fatih Porikli, Alexei Bourd, Amir Said | Date: 2025-04-11
Recently, 3D Gaussian Splatting (3DGS) has emerged as a significant advancement in 3D scene reconstruction, attracting considerable attention due to its ability to recover high-fidelity details while maintaining low complexity. Despite the promising results achieved by 3DGS, its rendering performanc ... more
-5.1246 AMAD: AutoMasked Attention for Unsupervised Multivariate Time Series Anomaly Detection
Authors: Tiange Huang, Yongjun Li | Date: 2025-04-11
Unsupervised multivariate time series anomaly detection (UMTSAD) plays a critical role in various domains, including finance, networks, and sensor systems. In recent years, due to the outstanding performance of deep learning in general sequential tasks, many models have been specialized for deep UMT ... more
-5.1256 CLaSP: Learning Concepts for Time-Series Signals from Natural Language Supervision
Authors: Aoi Ito, Kota Dohi, Yohei Kawaguchi | Date: 2025-04-11
This paper presents CLaSP, a novel model for retrieving time-series signals using natural language queries that describe signal characteristics. The ability to search time-series signals based on descriptive queries is essential in domains such as industrial diagnostics, where data scientists often ... more
-5.1261 Accurate Control under Voltage Drop for Rotor Drones
Authors: Yuhang Liu, Jindou Jia, Zihan Yang, Kexin Guo | Date: 2025-04-11
This letter proposes an anti-disturbance control scheme for rotor drones to counteract voltage drop (VD) disturbance caused by voltage drop of the battery, which is a common case for long-time flight or aggressive maneuvers. Firstly, the refined dynamics of rotor drones considering VD disturbance ar ... more
-5.1264 Using ML filters to help automated vulnerability repairs: when it helps and when it doesn't
Authors: Maria Camporese, Fabio Massacci | Date: 2025-04-11
[Context:] The acceptance of candidate patches in automated program repair has been typically based on testing oracles. Testing requires typically a costly process of building the application while ML models can be used to quickly classify patches, thus allowing more candidate patches to be generate ... more
-5.1266 DBaS-Log-MPPI: Efficient and Safe Trajectory Optimization via Barrier States
Authors: Fanxin Wang, Haolong Jiang, Chuyuan Tao, Wenbin Wan, Yikun Cheng | Date: 2025-04-11
Optimizing trajectory costs for nonlinear control systems remains a significant challenge. Model Predictive Control (MPC), particularly sampling-based approaches such as the Model Predictive Path Integral (MPPI) method, has recently demonstrated considerable success by leveraging parallel computing ... more
-5.1283 Wanting to be Understood
Authors: Chrisantha Fernando, Dylan Banarse, Simon Osindero | Date: 2025-04-11
This paper explores an intrinsic motivation for mutual awareness, hypothesizing that humans possess a fundamental drive to understand and to be understood even in the absence of extrinsic rewards. Through simulations of the perceptual crossing paradigm, we explore the effect of various internal rewa ... more
-5.1343 AWDIT: An Optimal Weak Database Isolation Tester
Authors: Lasse M{\o}ldrup, Andreas Pavlogiannis | Date: 2025-04-11
In order to achieve low latency, high throughput, and partition tolerance, modern databases forgo strong transaction isolation for weak isolation guarantees. However, several production databases have been found to suffer from isolation bugs, breaking their data-consistency contract. Black-box testi ... more
-5.1372 A posteriori error estimates for Schr{\"o}dinger operators discretized with linear combinations of atomic orbitals
Authors: Genevi\`eve Dusson (LMB), Mi-Song Dupuy (LJLL), Ioanna-Maria Lygatsika (CEA/DAM) | Date: 2025-04-11
We establish guaranteed and practically computable a posteriori error bounds for source problems and eigenvalue problems involving linear Schr{\"o}dinger operators with atom-centered potentials discretized with linear combinations of atomic orbitals. We show that the energy norm of the discretizatio ... more
-5.1381 EffOWT: Transfer Visual Language Models to Open-World Tracking Efficiently and Effectively
Authors: Bingyang Wang, Kaer Huang, Bin Li, Yiqiang Yan, Lihe Zhang, Huchuan Lu, You He | Date: 2025-04-11
Open-World Tracking (OWT) aims to track every object of any category, which requires the model to have strong generalization capabilities. Trackers can improve their generalization ability by leveraging Visual Language Models (VLMs). However, challenges arise with the fine-tuning strategies when VLM ... more
-5.1393 Agent-Arena: A General Framework for Evaluating Control Algorithms
Authors: Halid Abdulrahim Kadi, Kasim Terzi\'c | Date: 2025-04-11
Robotic research is inherently challenging, requiring expertise in diverse environments and control algorithms. Adapting algorithms to new environments often poses significant difficulties, compounded by the need for extensive hyper-parameter tuning in data-driven methods. To address these challenge ... more
-5.1402 A Year of the DSA Transparency Database: What it (Does Not) Reveal About Platform Moderation During the 2024 European Parliament Election
Authors: Gautam Kishore Shahi, Benedetta Tessa, Amaury Trujillo, Stefano Cresci | Date: 2025-04-11
Social media platforms face heightened risks during major political events; yet, how platforms adapt their moderation practices in response remains unclear. The Digital Services Act Transparency Database offers an unprecedented opportunity to systematically study content moderation at scale, enablin ... more
-5.1456 Control Node Placement and Structural Controllability of Water Quality Dynamics in Drinking Networks
Authors: Salma M. Elsherif, Ahmad F. Taha | Date: 2025-04-11
Chlorine, the most widely used disinfectant, needs to be adequately distributed in water distribution networks (WDNs) to maintain consistent residual levels and ensure water safety. This is performed through control node injections at the treatment plant via booster stations scattered in WDNs. While ... more
-5.1466 MultiDelete for Multimodal Machine Unlearning
Authors: Jiali Cheng, Hadi Amiri | Date: 2025-04-11
Machine Unlearning removes specific knowledge about training data samples from an already trained model. It has significant practical benefits, such as purging private, inaccurate, or outdated information from trained models without the need for complete re-training. Unlearning within a multimodal s ... more
-5.1471 Comparing Self-Disclosure Themes and Semantics to a Human, a Robot, and a Disembodied Agent
Authors: Sophie Chiang, Guy Laban, Emily S. Cross, Hatice Gunes | Date: 2025-04-11
As social robots and other artificial agents become more conversationally capable, it is important to understand whether the content and meaning of self-disclosure towards these agents changes depending on the agent's embodiment. In this study, we analysed conversational data from three controlled e ... more
-5.1474 Stochastic Ray Tracing of 3D Transparent Gaussians
Authors: Xin Sun, Iliyan Georgiev, Yun Fei, Milo\v{s} Ha\v{s}an | Date: 2025-04-11
3D Gaussian splatting has recently been widely adopted as a 3D representation for novel-view synthesis, relighting, and text-to-3D generation tasks, offering realistic and detailed results through a collection of explicit 3D Gaussians carrying opacities and view-dependent colors. However, efficient ... more
-5.1526 ShadowBinding: Realizing Effective Microarchitectures for In-Core Secure Speculation Schemes
Authors: Amund Bergland Kvalsvik, Magnus Sj\"alander | Date: 2025-04-11
Secure speculation schemes have shown great promise in the war against speculative side-channel attacks, and will be a key building block for developing secure, high-performance architectures moving forward. As the field matures, the need for rigorous microarchitectures, and corresponding performanc ... more
-5.1532 Data-driven Fuzzy Control for Time-Optimal Aggressive Trajectory Following
Authors: August Phelps, Juan Augusto Paredes Salazar, Ankit Goel | Date: 2025-04-11
Optimal trajectories that minimize a user-defined cost function in dynamic systems require the solution of a two-point boundary value problem. The optimization process yields an optimal control sequence that depends on the initial conditions and system parameters. However, the optimal sequence may r ... more
-5.1601 Free Random Projection for In-Context Reinforcement Learning
Authors: Tomohiro Hayase, Beno\^it Collins, Nakamasa Inoue | Date: 2025-04-11
Hierarchical inductive biases are hypothesized to promote generalizable policies in reinforcement learning, as demonstrated by explicit hyperbolic latent representations and architectures. Therefore, a more flexible approach is to have these biases emerge naturally from the algorithm. We introduce F ... more
-5.1683 Radiative Backpropagation with Non-Static Geometry
Authors: Markus Worchel, Ugo Finnendahl, Marc Alexa | Date: 2025-04-11
Radiative backpropagation-based methods efficiently compute reverse-mode derivatives in physically-based differentiable rendering by simulating the propagation of differential radiance. A key assumption is that differential radiance is transported like normal radiance. We observe that this holds onl ... more
-5.1805 Dynamic Relative Representations for Goal-Oriented Semantic Communications
Authors: Simone Fiorellino, Claudio Battiloro, Emilio Calvanese Strinati, Paolo Di Lorenzo | Date: 2025-04-11
In future 6G wireless networks, semantic and effectiveness aspects of communications will play a fundamental role, incorporating meaning and relevance into transmissions. However, obstacles arise when devices employ diverse languages, logic, or internal representations, leading to semantic mismatche ... more
-5.1808 EXCLAIM: An Explainable Cross-Modal Agentic System for Misinformation Detection with Hierarchical Retrieval
Authors: Yin Wu, Zhengxuan Zhang, Fuling Wang, Yuyu Luo, Hui Xiong, Nan Tang | Date: 2025-04-11
Misinformation continues to pose a significant challenge in today's information ecosystem, profoundly shaping public perception and behavior. Among its various manifestations, Out-of-Context (OOC) misinformation is particularly obscure, as it distorts meaning by pairing authentic images with mislead ... more
-5.1822 End-to-End Driving with Online Trajectory Evaluation via BEV World Model
Authors: Yingyan Li, Yuqi Wang, Yang Liu, Jiawei He, Lue Fan, Zhaoxiang Zhang | Date: 2025-04-11
End-to-end autonomous driving has achieved remarkable progress by integrating perception, prediction, and planning into a fully differentiable framework. Yet, to fully realize its potential, an effective online trajectory evaluation is indispensable to ensure safety. By forecasting the future outcom ... more
-5.1852 Physics informed neural network for forward and inverse modeling of low grade brain tumors
Authors: K. Murari, P. Roul, S. Sundar | Date: 2025-04-11
A low grade tumor is a slow growing tumor with a lower likelihood of spreading compared to high grade tumors. Mathematical modeling using partial differential equations (PDEs) plays a crucial role in describing tumor behavior, growth and progression. This study employs the Burgess and extended Fishe ... more
-5.1857 SDHN: Skewness-Driven Hypergraph Networks for Enhanced Localized Multi-Robot Coordination
Authors: Delin Zhao, Yanbo Shan, Chang Liu, Shenghang Lin, Yingxin Shou, Bin Xu | Date: 2025-04-11
Multi-Agent Reinforcement Learning is widely used for multi-robot coordination, where simple graphs typically model pairwise interactions. However, such representations fail to capture higher-order collaborations, limiting effectiveness in complex tasks. While hypergraph-based approaches enhance coo ... more
-5.1892 End2end-ALARA: Approaching the ALARA Law in CT Imaging with End-to-end Learning
Authors: Xi Tao, Liyan Lin | Date: 2025-04-11
Computed tomography (CT) examination poses radiation injury to patient. A consensus performing CT imaging is to make the radiation dose as low as reasonably achievable, i.e. the ALARA law. In this paper, we propose an end-to-end learning framework, named End2end-ALARA, that jointly optimizes dose mo ... more
-5.1893 LCL Resonance Analysis and Damping in Single-Loop Grid-Forming Wind Turbines
Authors: Meng Chen, Yufei Xi, Frede Blaabjerg, Lin Cheng, Ioannis Lestas | Date: 2025-04-11
A dynamic phenomenon known as LCL resonance is often neglected when stability analysis is carried out for grid-forming (GFM) control schemes by wind turbine systems, due to its high frequency. This paper shows that this simplification is not always valid for single-loop (SL) control schemes. A detai ... more
-5.1897 Supports for Outerplanar and Bounded Treewidth Graphs
Authors: Rajiv Raman, Karamjeet Singh | Date: 2025-04-11
We study the existence and construction of sparse supports for hypergraphs derived from subgraphs of a graph $G$. For a hypergraph $(X,\mathcal{H})$, a support $Q$ is a graph on $X$ s.t. $Q[H]$, the graph induced on vertices in $H$ is connected for every $H\in\mathcal{H}$.
-5.1941 Going beyond explainability in multi-modal stroke outcome prediction models
Authors: Jonas Br\"andli, Maurice Schneeberger, Lisa Herzog, Loran Avci, Nordin Dari, Martin H\"aansel, Hakim Baazaoui, Pascal B\"uhler, Susanne Wegener, Beate Sick | Date: 2025-04-11
Aim: This study aims to enhance interpretability and explainability of multi-modal prediction models integrating imaging and tabular patient data.
-5.1943 ASHiTA: Automatic Scene-grounded HIerarchical Task Analysis
Authors: Yun Chang, Leonor Fermoselle, Duy Ta, Bernadette Bucher, Luca Carlone, Jiuguang Wang | Date: 2025-04-11
While recent work in scene reconstruction and understanding has made strides in grounding natural language to physical 3D environments, it is still challenging to ground abstract, high-level instructions to a 3D scene. High-level instructions might not explicitly invoke semantic elements in the scen ... more
-5.1944 Solving Power System Problems using Adiabatic Quantum Computing
Authors: Zeynab Kaseb, Matthias Moller, Peter Palensky, Pedro P. Vergara | Date: 2025-04-11
This letter proposes a novel combinatorial optimization framework that reformulates existing power system problems into a format executable on quantum annealers. The proposed framework accommodates both normal and complex numbers and enables efficient handling of large-scale problems, thus ensuring ... more
-5.1952 A Case Study of Scalable Content Annotation Using Multi-LLM Consensus and Human Review
Authors: Mingyue Yuan, Jieshan Chen, Zhenchang Xing, Gelareh Mohammadi, Aaron Quigley | Date: 2025-04-11
Content annotation at scale remains challenging, requiring substantial human expertise and effort. This paper presents a case study in code documentation analysis, where we explore the balance between automation efficiency and annotation accuracy. We present MCHR (Multi-LLM Consensus with Human Revi ... more
-5.1965 STAGE: Stemmed Accompaniment Generation through Prefix-Based Conditioning
Authors: Giorgio Strano, Chiara Ballanti, Donato Crisostomi, Michele Mancusi, Luca Cosmo, Emanuele Rodol\`a | Date: 2025-04-11
Recent advances in generative models have made it possible to create high-quality, coherent music, with some systems delivering production-level output. Yet, most existing models focus solely on generating music from scratch, limiting their usefulness for musicians who want to integrate such models ... more
-5.2009 When is the partial map classifier a Sierpi\'nski cone?
Authors: Leoni Pugh, Jonathan Sterling | Date: 2025-04-11
We study the relationship between partial map classifiers, Sierpi\'nski cones, and axioms for synthetic higher categories and domains within univalent foundations. In particular, we show that synthetic $\infty$-categories are closed under partial map classifiers assuming Phoa's principle, and we iso ... more
-5.2012 Retuve: Automated Multi-Modality Analysis of Hip Dysplasia with Open Source AI
Authors: Adam McArthur, Stephanie Wichuk, Stephen Burnside, Andrew Kirby, Alexander Scammon, Damian Sol, Abhilash Hareendranathan, Jacob L. Jaremko | Date: 2025-04-11
Developmental dysplasia of the hip (DDH) poses significant diagnostic challenges, hindering timely intervention. Current screening methodologies lack standardization, and AI-driven studies suffer from reproducibility issues due to limited data and code availability. To address these limitations, we ... more
-5.2052 A Uniform Framework for Handling Position Constraints in String Solving (Technical Report)
Authors: Yu-Fang Chen, Vojt\v{e}ch Havlena, Michal He\v{c}ko, Luk\'a\v{s} Hol\'ik, Ond\v{r}ej Leng\'al | Date: 2025-04-11
We introduce a novel decision procedure for solving the class of position string constraints, which includes string disequalities, not-prefixof, not-suffixof, str$.$at, and not-str$.$at. These constraints are generated frequently in almost any application of string constraint solving. Our procedure ... more
-5.2055 Can we repurpose multiple-choice question-answering models to rerank retrieved documents?
Authors: Jasper Kyle Catapang | Date: 2025-04-11
Yes, repurposing multiple-choice question-answering (MCQA) models for document reranking is both feasible and valuable. This preliminary work is founded on mathematical parallels between MCQA decision-making and cross-encoder semantic relevance assessments, leading to the development of R*, a proof- ... more
-5.2094 Asymptotic Variance in the Central Limit Theorem for Multilevel Markovian Stochastic Approximation
Authors: Ajay Jasra, Abylay Zhumekenov | Date: 2025-04-11
In this note we consider the finite-dimensional parameter estimation problem associated to inverse problems. In such scenarios, one seeks to maximize the marginal likelihood associated to a Bayesian model. This latter model is connected to the solution of partial or ordinary differential equation. A ... more
-5.2111 SigChord: Sniffing Wide Non-sparse Multiband Signals for Terrestrial and Non-terrestrial Wireless Networks
Authors: Jinbo Peng, Junwen Duan, Zheng Lin, Haoxuan Yuan, Yue Gao, Zhe Chen | Date: 2025-04-11
While unencrypted information inspection in physical layer (e.g., open headers) can provide deep insights for optimizing wireless networks, the state-of-the-art (SOTA) methods heavily depend on full sampling rate (a.k.a Nyquist rate), and high-cost radios, due to terrestrial and non-terrestrial netw ... more
-5.2123 Bridging Research and Standardization: Innovations and Methodology for 6G Standard Contributions
Authors: Francesca Conserva, Fabio Busacca, Corrado Puligheddu, Simone Bizzarri, Maurizio Fodrini, Giampaolo Cuozzo, Riccardo Marini | Date: 2025-04-11
The transition towards 6G presents unique challenges and opportunities in mobile networks design and standardization. Addressing these challenges requires a robust methodology for analyzing and selecting innovations that can be effectively translated into 3rd Generation Partnership Project (3GPP) co ... more
-5.2234 Conformal Slit Mapping Based Spiral Tool Trajectory Planning for Ball-end Milling on Complex Freeform Surfaces
Authors: Changqing Shen, BingZhou Xu, Xiaojian Zhang, Sijie Yan, Han Ding | Date: 2025-04-11
This study presents a spiral-based complete coverage strategy for ball-end milling on freeform surfaces, utilizing conformal slit mapping to generate milling trajectories that are more compact, smoother, and evenly distributed when machining 2D cavities with islands. This approach, an upgrade from t ... more
-5.2251 A primal-dual perspective for distributed TD-learning
Authors: Han-Dong Lim, Donghwan Lee | Date: 2025-04-11
The goal of this paper is to investigate distributed temporal difference (TD) learning for a networked multi-agent Markov decision process. The proposed approach is based on distributed optimization algorithms, which can be interpreted as primal-dual Ordinary differential equation (ODE) dynamics sub ... more
-5.2258 Approximate Feedback Nash Equilibria with Sparse Inter-Agent Dependencies
Authors: Xinjie Liu, Jingqi Li, Filippos Fotiadis, Mustafa O. Karabag, Jesse Milzman, David Fridovich-Keil, Ufuk Topcu | Date: 2025-04-11
Feedback Nash equilibrium strategies in multi-agent dynamic games require availability of all players' state information to compute control actions. However, in real-world scenarios, sensing and communication limitations between agents make full state feedback expensive or impractical, and such stra ... more
-5.2296 Crafting Query-Aware Selective Attention for Single Image Super-Resolution
Authors: Junyoung Kim, Youngrok Kim, Siyeol Jung, Donghyun Min | Date: 2025-04-11
Single Image Super-Resolution (SISR) reconstructs high-resolution images from low-resolution inputs, enhancing image details. While Vision Transformer (ViT)-based models improve SISR by capturing long-range dependencies, they suffer from quadratic computational costs or employ selective attention me ... more
-5.2328 Variable Metric Splitting Methods for Neuromorphic Circuits Simulation
Authors: Amir Shahhosseini, Thomas Burger, Rodolphe Sepulchre | Date: 2025-04-11
This paper proposes a variable metric splitting algorithm to solve the electrical behavior of neuromorphic circuits made of capacitors, memristive elements, and batteries. The gradient property of the memristive elements is exploited to split the current to voltage operator as the sum of the derivat ... more
-5.2329 Distributional Autoencoders Know the Score
Authors: Andrej Leban | Date: 2025-04-11
This work presents novel and desirable properties of a recently introduced class of autoencoders - the Distributional Principal Autoencoder (DPA) - which combines distributionally correct reconstruction with principal components-like interpretability of the encodings. First, we show formally that th ... more
-5.2338 ZETA: a library for Zonotope-based EsTimation and fAult diagnosis of discrete-time systems
Authors: Brenner S. Rego, Joseph K. Scott, Davide M. Raimondo, Marco H. Terra, Guilherme V. Raffo | Date: 2025-04-11
This paper introduces ZETA, a new MATLAB library for Zonotope-based EsTimation and fAult diagnosis of discrete-time systems. It features user-friendly implementations of set representations based on zonotopes, namely zonotopes, constrained zonotopes, and line zonotopes, in addition to a basic implem ... more
-5.2367 Artificial Intelligence for Pediatric Height Prediction Using Large-Scale Longitudinal Body Composition Data
Authors: Dohyun Chun, Hae Woon Jung, Jongho Kang, Woo Young Jang, Jihun Kim | Date: 2025-04-11
This study developed an accurate artificial intelligence model for predicting future height in children and adolescents using anthropometric and body composition data from the GP Cohort Study (588,546 measurements from 96,485 children aged 7-18). The model incorporated anthropometric measures, body ... more
-5.2391 RayFronts: Open-Set Semantic Ray Frontiers for Online Scene Understanding and Exploration
Authors: Omar Alama, Avigyan Bhattacharya, Haoyang He, Seungchan Kim, Yuheng Qiu, Wenshan Wang, Cherie Ho, Nikhil Keetha, Sebastian Scherer | Date: 2025-04-11
Open-set semantic mapping is crucial for open-world robots. Current mapping approaches either are limited by the depth range or only map beyond-range entities in constrained settings, where overall they fail to combine within-range and beyond-range observations. Furthermore, these methods make a tra ... more
-5.2399 Identifying Unknown Stochastic Dynamics via Finite expression methods
Authors: Senwei Liang, Chunmei Wang, Xingjian Xu | Date: 2025-04-11
Modeling stochastic differential equations (SDEs) is crucial for understanding complex dynamical systems in various scientific fields. Recent methods often employ neural network-based models, which typically represent SDEs through a combination of deterministic and stochastic terms. However, these m ... more
-5.2402 Holistic Fusion: Task- and Setup-Agnostic Robot Localization and State Estimation with Factor Graphs
Authors: Julian Nubert, Turcan Tuna, Jonas Frey, Cesar Cadena, Katherine J. Kuchenbecker, Shehryar Khattak, Marco Hutter | Date: 2025-04-11
Seamless operation of mobile robots in challenging environments requires low-latency local motion estimation (e.g., dynamic maneuvers) and accurate global localization (e.g., wayfinding). While most existing sensor-fusion approaches are designed for specific scenarios, this work introduces a flexibl ... more
-5.2489 Advancing Remote Medical Palpation through Cognition and Emotion
Authors: Matti Itkonen, Shotaro Okajima, Sayako Ueda, Alvaro Costa-Garcia, Yang Ningjia, Tadatoshi Kurogi, Takeshi Fujiwara, Shigeru Kurimoto, Shintaro Oyama, Masaomi Saeki, Michiro Yamamoto, Hidemasa Yoneda, Hitoshi Hirata, Shingo Shimoda | Date: 2025-04-11
This paper explores the cognitive and emotional processes involved in medical palpation to develop a more effective remote palpation system. Conventional remote palpation systems primarily rely on force feedback to convey a patient's tactile condition to doctors. However, an analysis of the palpatio ... more
-5.2521 Low-Rank Mirror-Prox for Nonsmooth and Low-Rank Matrix Optimization Problems
Authors: Dan Garber, Atara Kaplan | Date: 2025-04-11
Low-rank and nonsmooth matrix optimization problems capture many fundamental tasks in statistics and machine learning. While significant progress has been made in recent years in developing efficient methods for \textit{smooth} low-rank optimization problems that avoid maintaining high-rank matrices ... more
-5.2525 Beyond the Winding Path of Learning: Exploring Affective, Cognitive, and Action-Oriented Prompts for Communication Skills
Authors: Naoko Hayashida | Date: 2025-04-11
Since high dropout rates in online learning platforms were reported, various factors affecting learner retention have been identified, with learners' perceptions of their experiences playing a crucial role in shaping their persistence. For instance, Kittur et al. highlight how success expectations a ... more
-5.2545 Mosaic: Composite Projection Pruning for Resource-efficient LLMs
Authors: Bailey J. Eccles, Leon Wong, Blesson Varghese | Date: 2025-04-11
Extensive compute and memory requirements limit the deployment of large language models (LLMs) on any hardware. Compression methods, such as pruning, can reduce model size, which in turn reduces resource requirements. State-of-the-art pruning is based on coarse-grained methods. They are time-consumi ... more
-5.2547 Adapting GT2-FLS for Uncertainty Quantification: A Blueprint Calibration Strategy
Authors: Yusuf Guven, Tufan Kumbasar | Date: 2025-04-11
Uncertainty Quantification (UQ) is crucial for deploying reliable Deep Learning (DL) models in high-stakes applications. Recently, General Type-2 Fuzzy Logic Systems (GT2-FLSs) have been proven to be effective for UQ, offering Prediction Intervals (PIs) to capture uncertainty. However, existing meth ... more
-5.2687 Learning global control of underactuated systems with Model-Based Reinforcement Learning
Authors: Niccol\`o Turcato, Marco Cal\`i, Alberto Dalla Libera, Giulio Giacomuzzo, Ruggero Carli, Diego Romeres | Date: 2025-04-11
This short paper describes our proposed solution for the third edition of the "AI Olympics with RealAIGym" competition, held at ICRA 2025. We employed Monte-Carlo Probabilistic Inference for Learning Control (MC-PILCO), an MBRL algorithm recognized for its exceptional data efficiency across various ... more
-5.2696 A Global Analysis of the Primal-Dual Method for Pliable Families
Authors: Ishan Bansal | Date: 2025-04-11
We study a core algorithmic problem in network design called ${F}$-augmentation that involves increasing the connectivity of a given family of cuts ${F}$. Over 30 years ago, Williamson et al. (STOC `93) provided a 2-approximation primal-dual algorithm when ${F}$ is a so-called uncrossable family but ... more
-5.2754 Extended Version: Multi-Robot Motion Planning with Cooperative Localization
Authors: Anne Theurkauf, Nisar Ahmed, Morteza Lahijanian | Date: 2025-04-11
We consider the uncertain multi-robot motion planning (MRMP) problem with cooperative localization (CL-MRMP), under both motion and measurement noise, where each robot can act as a sensor for its nearby teammates. We formalize CL-MRMP as a chance-constrained motion planning problem, and propose a sa ... more
-5.2795 SpeechCap: Leveraging Playful Impact Captions to Facilitate Interpersonal Communication in Social Virtual Reality
Authors: Yu Zhang, Yi Wen, Siying Hu, Zhicong Lu | Date: 2025-04-11
Social Virtual Reality (VR) emerges as a promising platform bringing immersive, interactive, and engaging mechanisms for collaborative activities in virtual spaces. However, interpersonal communication in social VR is still limited with existing mediums and channels. To bridge the gap, we propose a ... more
-5.2811 Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence
Authors: Yu Qiao, Apurba Adhikary, Huy Q. Le, Eui-Nam Huh, Zhu Han, Choong Seon Hong | Date: 2025-04-11
Federated learning (FL) has gained significant attention for enabling decentralized training on edge networks without exposing raw data. However, FL models remain susceptible to adversarial attacks and performance degradation in non-IID data settings, thus posing challenges to both robustness and ac ... more
-5.292 Countering threats to national security posed by AI systems through an incident regime
Authors: Alejandro Ortega | Date: 2025-04-11
Recent progress in AI capabilities has heightened concerns that AI systems could pose a threat to national security, for example, by making it easier for malicious actors to perform cyberattacks on critical national infrastructure, or through loss of control of autonomous AI systems. In parallel, fe ... more
-5.293 Defending LLM Watermarking Against Spoofing Attacks with Contrastive Representation Learning
Authors: Li An, Yujian Liu, Yepeng Liu, Yang Zhang, Yuheng Bu, Shiyu Chang | Date: 2025-04-11
Watermarking has emerged as a promising technique for detecting texts generated by LLMs. Current research has primarily focused on three design criteria: high quality of the watermarked text, high detectability, and robustness against removal attack. However, the security against spoofing attacks re ... more
-5.2946 WaveHiTS: Wavelet-Enhanced Hierarchical Time Series Modeling for Wind Direction Nowcasting in Eastern Inner Mongolia
Authors: Hailong Shu, Weiwei Song, Yue Wang, Jiping Zhang | Date: 2025-04-11
Wind direction forecasting plays a crucial role in optimizing wind energy production, but faces significant challenges due to the circular nature of directional data, error accumulation in multi-step forecasting, and complex meteorological interactions. This paper presents a novel model, WaveHiTS, w ... more
-5.2949 An Information-Geometric Approach to Artificial Curiosity
Authors: Alexander Nedergaard, Pablo A. Morales | Date: 2025-04-11
Learning in environments with sparse rewards remains a fundamental challenge in reinforcement learning. Artificial curiosity addresses this limitation through intrinsic rewards to guide exploration, however, the precise formulation of these rewards has remained elusive. Ideally, such rewards should ... more
-5.2978 Multispectral Demosaicing via Dual Cameras
Authors: SaiKiran Tedla, Junyong Lee, Beixuan Yang, Mahmoud Afifi, Michael S. Brown | Date: 2025-04-11
Multispectral (MS) images capture detailed scene information across a wide range of spectral bands, making them invaluable for applications requiring rich spectral data. Integrating MS imaging into multi camera devices, such as smartphones, has the potential to enhance both spectral applications and ... more
-5.2994 Coreset Strikes Back: Improved Parameterized Approximation Schemes for (Constrained) k-Median/Means
Authors: Sujoy Bhore, Ameet Gadekar, Tanmay Inamdar | Date: 2025-04-11
Algorithmic scatter dimension is a notion of metric spaces introduced recently by Abbasi et al. (FOCS 2023), which unifies many well-known metric spaces, including continuous Euclidean space, bounded doubling space, planar and bounded treewidth metrics. Recently, Bourneuf and Pilipczuk (SODA 2025) s ... more
-5.3066 ActiView: Evaluating Active Perception Ability for Multimodal Large Language Models
Authors: Ziyue Wang, Chi Chen, Fuwen Luo, Yurui Dong, Yuanchi Zhang, Yuzhuang Xu, Xiaolong Wang, Peng Li, Yang Liu | Date: 2025-04-11
Active perception, a crucial human capability, involves setting a goal based on the current understanding of the environment and performing actions to achieve that goal. Despite significant efforts in evaluating Multimodal Large Language Models (MLLMs), active perception has been largely overlooked. ... more
-5.3094 Oil Spill Segmentation using Deep Encoder-Decoder models
Authors: Abhishek Ramanathapura Satyanarayana, Maruf A. Dhali | Date: 2025-04-11
Crude oil is an integral component of the world economy and transportation sectors. With the growing demand for crude oil due to its widespread applications, accidental oil spills are unfortunate yet unavoidable. Even though oil spills are difficult to clean up, the first and foremost challenge is t ... more
-5.3184 Scalable mixed-domain Gaussian process modeling and model reduction for longitudinal data
Authors: Juho Timonen, Harri L\"ahdesm\"aki | Date: 2025-04-11
Gaussian process (GP) models that combine both categorical and continuous input variables have found use in analysis of longitudinal data and computer experiments. However, standard inference for these models has the typical cubic scaling, and common scalable approximation schemes for GPs cannot be ... more
-5.3285 Identifying Key Challenges of Hardness-Based Resampling
Authors: Pawel Pukowski, Venet Osmani | Date: 2025-04-11
Performance gap across classes remains a persistent challenge in machine learning, often attributed to variations in class hardness. One way to quantify class hardness is through sample complexity - the minimum number of samples required to effectively learn a given class. Sample complexity theory s ... more
-5.337 MovSAM: A Single-image Moving Object Segmentation Framework Based on Deep Thinking
Authors: Chang Nie, Yiqing Xu, Guangming Wang, Zhe Liu, Yanzi Miao, Hesheng Wang | Date: 2025-04-11
Moving object segmentation plays a vital role in understanding dynamic visual environments. While existing methods rely on multi-frame image sequences to identify moving objects, single-image MOS is critical for applications like motion intention prediction and handling camera frame drops. However, ... more
-5.3416 Assessment of FAIR (Findability, Accessibility, Interoperability, and Reusability) data implementation frameworks: a parametric approach
Authors: Ranjeet Kumar Singh, Akanksha Nagpal, Arun Jadhav, Devika P. Madalli | Date: 2025-04-11
Open science movement has established reproducibility, transparency, and validation of research outputs as essential norms for conducting scientific research. It advocates for open access to research outputs, especially research data, to enable verification of published findings and its optimum reus ... more
-5.3447 TXSQL: Lock Optimizations Towards High Contented Workloads (Extended Version)
Authors: Donghui Wang, Yuxing Chen, Chengyao Jiang, Anqun Pan, Wei Jiang, Songli Wang, Hailin Lei, Chong Zhu, Lixiong Zheng, Wei Lu, Yunpeng Chai, Feng Zhang, Xiaoyong Du | Date: 2025-04-11
Two-phase locking (2PL) is a fundamental and widely used concurrency control protocol. It regulates concurrent access to database data by following a specific sequence of acquiring and releasing locks during transaction execution, thereby ensuring transaction isolation. However, in strict 2PL, trans ... more
-5.3468 Ternarization of Vision Language Models for use on edge devices
Authors: Ben Crulis, Cyril De Runz, Barthelemy Serres, Gilles Venturini | Date: 2025-04-11
We propose a process to compress a pre-trained Vision Language Model into a ternary version of itself instead of training a ternary model from scratch. A new initialization scheme from pre-trained weights based on the k-means algorithm is proposed to reduce the ternarization time. We implement diffe ... more
-5.3472 Classifying Subjective Time Perception in a Multi-robot Control Scenario Using Eye-tracking Information
Authors: Till Aust, Julian Kaduk, Heiko Hamann | Date: 2025-04-11
As automation and mobile robotics reshape work environments, rising expectations for productivity increase cognitive demands on human operators, leading to potential stress and cognitive overload. Accurately assessing an operator's mental state is critical for maintaining performance and well-being. ... more
-5.3549 Learning Flatness-Preserving Residuals for Pure-Feedback Systems
Authors: Fengjun Yang, Jake Welde, Nikolai Matni | Date: 2025-04-11
We study residual dynamics learning for differentially flat systems, where a nominal model is augmented with a learned correction term from data. A key challenge is that generic residual parameterizations may destroy flatness, limiting the applicability of flatness-based planning and control methods ... more
-5.3615 Dynamic Residual Safe Reinforcement Learning for Multi-Agent Safety-Critical Scenarios Decision-Making
Authors: Kaifeng Wang, Yinsong Chen, Qi Liu, Xueyuan Li, Xin Gao | Date: 2025-04-11
In multi-agent safety-critical scenarios, traditional autonomous driving frameworks face significant challenges in balancing safety constraints and task performance. These frameworks struggle to quantify dynamic interaction risks in real-time and depend heavily on manual rules, resulting in low comp ... more
-5.3637 Computably discrete represented spaces
Authors: Eike Neumann, Arno Pauly, C\'ecilia Pradic, Manlio Valenti | Date: 2025-04-11
In computable topology, a represented space is called computably discrete if its equality predicate is semidecidable. While any such space is classically isomorphic to an initial segment of the natural numbers, the computable-isomorphism types of computably discrete represented spaces exhibit a rich ... more
-5.3664 A Deep Single Image Rectification Approach for Pan-Tilt-Zoom Cameras
Authors: Teng Xiao, Qi Hu, Qingsong Yan, Wei Liu, Zhiwei Ye, Fei Deng | Date: 2025-04-11
Pan-Tilt-Zoom (PTZ) cameras with wide-angle lenses are widely used in surveillance but often require image rectification due to their inherent nonlinear distortions. Current deep learning approaches typically struggle to maintain fine-grained geometric details, resulting in inaccurate rectification. ... more
-5.3682 Implementation of a Zed 2i Stereo Camera for High-Frequency Shoreline Change and Coastal Elevation Monitoring
Authors: Jos\'e A. Pilartes-Congo, Matthew Kastl, Michael J. Starek, Marina Vicens-Miquel, Philippe Tissot | Date: 2025-04-11
The increasing population, thus financial interests, in coastal areas have increased the need to monitor coastal elevation and shoreline change. Though several resources exist to obtain this information, they often lack the required temporal resolution for short-term monitoring (e.g., every hour). T ... more
-5.3689 Longitudinal Assessment of Lung Lesion Burden in CT
Authors: Tejas Sudharshan Mathai, Benjamin Hou, Ronald M. Summers | Date: 2025-04-11
In the U.S., lung cancer is the second major cause of death. Early detection of suspicious lung nodules is crucial for patient treatment planning, management, and improving outcomes. Many approaches for lung nodule segmentation and volumetric analysis have been proposed, but few have looked at longi ... more
-5.3712 Image registration of 2D optical thin sections in a 3D porous medium: Application to a Berea sandstone digital rock image
Authors: Jaehong Chung, Wei Cai, Tapan Mukerji | Date: 2025-04-11
This study proposes a systematic image registration approach to align 2D optical thin-section images within a 3D digital rock volume. Using template image matching with differential evolution optimization, we identify the most similar 2D plane in 3D. The method is validated on a synthetic porous med ... more
-5.3819 Well2Flow: Reconstruction of reservoir states from sparse wells using score-based generative models
Authors: Shiqin Zeng, Haoyun Li, Abhinav Prakash Gahlot, Felix J. Herrmann | Date: 2025-04-11
This study investigates the use of score-based generative models for reservoir simulation, with a focus on reconstructing spatially varying permeability and saturation fields in saline aquifers, inferred from sparse observations at two well locations. By modeling the joint distribution of permeabili ... more
-5.3865 A Control-Oriented Simplified Single Particle Model with Grouped Parameter and Sensitivity Analysis for Lithium-Ion Batteries
Authors: Feng Guo, Luis D. Couto | Date: 2025-04-11
Lithium-ion batteries are widely used in transportation, energy storage, and consumer electronics, driving the need for reliable battery management systems (BMS) for state estimation and control. The Single Particle Model (SPM) balances computational efficiency and accuracy but faces challenges in p ... more
-5.387 Maximizing Battery Storage Profits via High-Frequency Intraday Trading
Authors: David Schaurecker, David Wozabal, Nils L\"ohndorf, Thorsten Staake | Date: 2025-04-11
Maximizing revenue for grid-scale battery energy storage systems in continuous intraday electricity markets requires strategies that are able to seize trading opportunities as soon as new information arrives. This paper introduces and evaluates an automated high-frequency trading strategy for batter ... more
-5.3884 Towards practicable Machine Learning development using AI Engineering Blueprints
Authors: Nicolas Weeger, Annika Stiehl, J\'oakim vom Kistowski, Stefan Gei{\ss}els\"oder, Christian Uhl | Date: 2025-04-11
The implementation of artificial intelligence (AI) in business applications holds considerable promise for significant improvements. The development of AI systems is becoming increasingly complex, thereby underscoring the growing importance of AI engineering and MLOps techniques. Small and medium-si ... more
-5.3888 Numerical Fuzz: A Type System for Rounding Error Analysis
Authors: Ariel E. Kellison, Justin Hsu | Date: 2025-04-11
Algorithms operating on real numbers are implemented as floating-point computations in practice, but floating-point operations introduce roundoff errors that can degrade the accuracy of the result. We propose $\Lambda_{num}$, a functional programming language with a type system that can express quan ... more
-5.3896 Rethinking LayerNorm in Image Restoration Transformers
Authors: MinKyu Lee, Sangeek Hyun, Woojin Jun, Hyunjun Kim, Jiwoo Chung, Jae-Pil Heo | Date: 2025-04-11
This work investigates abnormal feature behaviors observed in image restoration (IR) Transformers. Specifically, we identify two critical issues: feature entropy becoming excessively small and feature magnitudes diverging up to a million-fold scale. We pinpoint the root cause to the per-token normal ... more
-5.3938 A Cross-Domain Few-Shot Learning Method Based on Domain Knowledge Mapping
Authors: Jiajun Chen, Hongpeng Yin, Yifu Yang | Date: 2025-04-11
In task-based few-shot learning paradigms, it is commonly assumed that different tasks are independently and identically distributed (i.i.d.). However, in real-world scenarios, the distribution encountered in few-shot learning can significantly differ from the distribution of existing data. Thus, ho ... more
-5.4 FETTA: Flexible and Efficient Hardware Accelerator for Tensorized Neural Network Training
Authors: Jinming Lu, Jiayi Tian, Hai Li, Ian Young, Zheng Zhang | Date: 2025-04-11
The increasing demand for on-device training of deep neural networks (DNNs) aims to leverage personal data for high-performance applications while addressing privacy concerns and reducing communication latency. However, resource-constrained platforms face significant challenges due to the intensive ... more
-5.4014 Safe Navigation in Uncertain Crowded Environments Using Risk Adaptive CVaR Barrier Functions
Authors: Xinyi Wang, Taekyung Kim, Bardh Hoxha, Georgios Fainekos, Dimitra Panagou | Date: 2025-04-11
Robot navigation in dynamic, crowded environments poses a significant challenge due to the inherent uncertainties in the obstacle model. In this work, we propose a risk-adaptive approach based on the Conditional Value-at-Risk Barrier Function (CVaR-BF), where the risk level is automatically adjusted ... more
-5.4044 Zero-Order Control Barrier Functions for Sampled-Data Systems with State and Input Dependent Safety Constraints
Authors: Xiao Tan, Ersin Das, Aaron D. Ames, Joel W. Burdick | Date: 2025-04-11
We propose a novel zero-order control barrier function (ZOCBF) for sampled-data systems to ensure system safety. Our formulation generalizes conventional control barrier functions and straightforwardly handles safety constraints with high-relative degrees or those that explicitly depend on both syst ... more
-5.4093 Digital Twin Aided Channel Estimation: Zone-Specific Subspace Prediction and Calibration
Authors: Sadjad Alikhani, Ahmed Alkhateeb | Date: 2025-04-11
Effective channel estimation in sparse and high-dimensional environments is essential for next-generation wireless systems, particularly in large-scale MIMO deployments. This paper introduces a novel framework that leverages digital twins (DTs) as priors to enable efficient zone-specific subspace-ba ... more
-5.4139 TRIDENT: Tri-modal Real-time Intrusion Detection Engine for New Targets
Authors: Ildi Alla, Selma Yahia, Valeria Loscri | Date: 2025-04-11
The increasing availability of drones and their potential for malicious activities pose significant privacy and security risks, necessitating fast and reliable detection in real-world environments. However, existing drone detection systems often struggle in real-world settings due to environmental n ... more
-5.4274 Green building blocks reveal the complex anatomy of climate change mitigation technologies
Authors: Yang Li, Frank Neffke | Date: 2025-04-11
Climate-change mitigating innovation is considered essential for the world's transition toward a sustainable global economy. To guide this transition, integrated assessment models map sectoral emissions reduction targets into long-term trajectories towards carbon neutrality at the macro-level, while ... more
-5.4328 Saliency-driven Dynamic Token Pruning for Large Language Models
Authors: Yao Tao, Yehui Tang, Yun Wang, Mingjian Zhu, Hailin Hu, Yunhe Wang | Date: 2025-04-11
Despite the recent success of large language models (LLMs), LLMs are particularly challenging in long-sequence inference scenarios due to the quadratic computational complexity of the attention mechanism. Inspired by the interpretability theory of feature attribution in neural network models, we obs ... more
-5.4368 Unified CNNs and transformers underlying learning mechanism reveals multi-head attention modus vivendi
Authors: Ella Koresh, Ronit D. Gross, Yuval Meir, Yarden Tzach, Tal Halevi, Ido Kanter | Date: 2025-04-11
Convolutional neural networks (CNNs) evaluate short-range correlations in input images which progress along the layers, whereas vision transformer (ViT) architectures evaluate long-range correlations, using repeated transformer encoders composed of fully connected layers. Both are designed to solve ... more
-5.4377 Off-the-grid learning of mixtures from a continuous dictionary
Authors: Cristina Butucea (CREST, FAIRPLAY), Jean-Fran\c{c}ois Delmas (CERMICS), Anne Dutfoy (EDF R\&D), Cl\'ement Hardy (CERMICS, EDF R\&D) | Date: 2025-04-11
We consider a general non-linear model where the signal is a finite mixture of an unknown, possibly increasing, number of features issued from a continuous dictionary parameterized by a real non-linear parameter. The signal is observed with Gaussian (possibly correlated) noise in either a continuous ... more
-5.438 GenCAD: Image-Conditioned Computer-Aided Design Generation with Transformer-Based Contrastive Representation and Diffusion Priors
Authors: Md Ferdous Alam, Faez Ahmed | Date: 2025-04-11
The creation of manufacturable and editable 3D shapes through Computer-Aided Design (CAD) remains a highly manual and time-consuming task, hampered by the complex topology of boundary representations of 3D solids and unintuitive design tools. While most work in the 3D shape generation literature foc ... more
-5.4384 GastCoCo: Graph Storage and Coroutine-Based Prefetch Co-Design for Dynamic Graph Processing
Authors: Hongfu Li, Qian Tao, Song Yu, Shufeng Gong, Yanfeng Zhang, Feng Yao, Wenyuan Yu, Ge Yu, Jingren Zhou | Date: 2025-04-11
An efficient data structure is fundamental to meeting the growing demands in dynamic graph processing. However, the dual requirements for graph computation efficiency (with contiguous structures) and graph update efficiency (with linked list-like structures) present a conflict in the design principl ... more
-5.4482 Tight bounds on depth-2 QAC-circuits computing parity
Authors: Stephen Fenner, Daniel Grier, Daniel Pad\'e, Thomas Thierauf | Date: 2025-04-11
We show that the parity of more than three non-target input bits cannot be computed by QAC-circuits of depth-2, not even uncleanly, regardless of the number of ancilla qubits. This result is incomparable with other recent lower bounds on constant-depth QAC-circuits by Rosenthal [ICTS~2021,arXiv:2008 ... more
-5.4508 A Scalable Automatic Model Generation Tool for Cyber-Physical Network Topologies and Data Flows for Large-Scale Synthetic Power Grid Models
Authors: Samantha Israel, Sanjana Kunkolienkar, Ana Goulart, Kate Davis, Thomas Overbye | Date: 2025-04-11
Power grids and their cyber infrastructure are classified as Critical Energy Infrastructure/Information (CEII) and are not publicly accessible. While realistic synthetic test cases for power systems have been developed in recent years, they often lack corresponding cyber network models. This work ex ... more
-5.4516 Mitigating Adversarial Effects of False Data Injection Attacks in Power Grid
Authors: Farhin Farhad Riya, Shahinul Hoque, Yingyuan Yang, Jiangnan Li, Jinyuan Stella Sun, Hairong Qi | Date: 2025-04-11
Deep Neural Networks have proven to be highly accurate at a variety of tasks in recent years. The benefits of Deep Neural Networks have also been embraced in power grids to detect False Data Injection Attacks (FDIA) while conducting critical tasks like state estimation. However, the vulnerabilities ... more
-5.4528 Corrected with the Latest Version: Make Robust Asynchronous Federated Learning Possible
Authors: Chaoyi Lu, Yiding Sun, Pengbo Li, Zhichuan Yang | Date: 2025-04-11
As an emerging paradigm of federated learning, asynchronous federated learning offers significant speed advantages over traditional synchronous federated learning. Unlike synchronous federated learning, which requires waiting for all clients to complete updates before aggregation, asynchronous feder ... more
Federated Learning
-5.4556 Performance Analysis and Low-Complexity Beamforming Design for Near-Field Physical Layer Security
Authors: Yunpu Zhang, Yuan Fang, Changsheng You, Ying-Jun Angela Zhang, Hing Cheung So | Date: 2025-04-11
Extremely large-scale arrays (XL-arrays) have emerged as a key enabler in achieving the unprecedented performance requirements of future wireless networks, leading to a significant increase in the range of the near-field region. This transition necessitates the spherical wavefront model for characte ... more
-5.4619 MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning
Authors: Ylli Sadikaj, Hongkuan Zhou, Lavdim Halilaj, Stefan Schmid, Steffen Staab, Claudia Plant | Date: 2025-04-11
Precise optical inspection in industrial applications is crucial for minimizing scrap rates and reducing the associated costs. Besides merely detecting if a product is anomalous or not, it is crucial to know the distinct type of defect, such as a bent, cut, or scratch. The ability to recognize the " ... more
-5.4619 AI-Assisted Transport of Radioactive Ion Beams
Authors: Sergio Lopez-Caceres, Daniel Santiago-Gonzalez | Date: 2025-04-11
Beams of radioactive heavy ions allow researchers to study rare and unstable atomic nuclei, shedding light into the internal structure of exotic nuclei and on how chemical elements are formed in stars. However, the extraction and transport of radioactive beams rely on time-consuming expert-driven tu ... more
-5.4649 EchoONE: Segmenting Multiple echocardiography Planes in One Model
Authors: Jiongtong Hu, Wei Zhuo, Jun Cheng, Yingying Liu, Wufeng Xue, Dong Ni | Date: 2025-04-11
In clinical practice of echocardiography examinations, multiple planes containing the heart structures of different view are usually required in screening, diagnosis and treatment of cardiac disease. AI models for echocardiography have to be tailored for each specific plane due to the dramatic struc ... more
-5.4886 Sim-to-Real of Humanoid Locomotion Policies via Joint Torque Space Perturbation Injection
Authors: Woohyun Cha, Junhyeok Cha, Jaeyong Shin, Donghyeon Kim, Jaeheung Park | Date: 2025-04-11
This paper proposes a novel alternative to existing sim-to-real methods for training control policies with simulated experiences. Prior sim-to-real methods for legged robots mostly rely on the domain randomization approach, where a fixed finite set of simulation parameters is randomized during train ... more
-5.4893 Between the deterministic and non-deterministic query complexity
Authors: D\'aniel Gerbner | Date: 2025-04-11
We consider problems that can be solved by asking certain queries. The deterministic query complexity $D(P,n)$ of a problem $P$ is the smallest number of queries needed to ask in order to find the solution with an input of size $n$ (in the worst case), while the non-deterministic query complexity $D ... more
-5.4998 Induced Model Matching: Restricted Models Help Train Full-Featured Models
Authors: Usama Muneeb, Mesrob I. Ohannessian | Date: 2025-04-11
We consider scenarios where a very accurate (often small) predictive model using restricted features is available when training a full-featured (often larger) model. This restricted model may be thought of as side-information'', and can come either from an auxiliary dataset or from the same dataset ... more
-5.5071 A Cascaded Architecture for Extractive Summarization of Multimedia Content via Audio-to-Text Alignment
Authors: Tanzir Hossain, Ar-Rafi Islam, Md. Sabbir Hossain, Annajiat Alim Rasel | Date: 2025-04-11
This study presents a cascaded architecture for extractive summarization of multimedia content via audio-to-text alignment. The proposed framework addresses the challenge of extracting key insights from multimedia sources like YouTube videos. It integrates audio-to-text conversion using Microsoft Az ... more
-5.5082 JailDAM: Jailbreak Detection with Adaptive Memory for Vision-Language Model
Authors: Yi Nian, Shenzhe Zhu, Yuehan Qin, Li Li, Ziyi Wang, Chaowei Xiao, Yue Zhao | Date: 2025-04-11
Multimodal large language models (MLLMs) excel in vision-language tasks but also pose significant risks of generating harmful content, particularly through jailbreak attacks. Jailbreak attacks refer to intentional manipulations that bypass safety mechanisms in models, leading to the generation of in ... more
-5.5167 Scalable Geometric Learning with Correlation-Based Functional Brain Networks
Authors: Kisung You, Yelim Lee, Hae-Jeong Park | Date: 2025-04-11
The correlation matrix is a central representation of functional brain networks in neuroimaging. Traditional analyses often treat pairwise interactions independently in a Euclidean setting, overlooking the intrinsic geometry of correlation matrices. While earlier attempts have embraced the quotient ... more
-5.5253 Cooperative Dilemmas in Rational Debate
Authors: Toby Handfield, Juli\'an Garcia, Christian Hilbe, Shang Long Yeo | Date: 2025-04-11
As an epistemic activity, rational debate and discussion requires cooperation, yet involves a tension between collective and individual interests. While all participants benefit from collective outcomes like reaching consensus on true beliefs, individuals face personal costs when changing their mind ... more
-5.5275 A Diverse and Effective Retrieval-Based Debt Collection System with Expert Knowledge
Authors: Jiaming Luo, Weiyi Luo, Guoqing Sun, Mengchen Zhu, Haifeng Tang, Kunyao Lan, Mengyue Wu, Kenny Q. Zhu | Date: 2025-04-11
Designing effective debt collection systems is crucial for improving operational efficiency and reducing costs in the financial industry. However, the challenges of maintaining script diversity, contextual relevance, and coherence make this task particularly difficult. This paper presents a debt col ... more
-5.5286 Optimal Sensor Placement Using Combinations of Hybrid Measurements for Source Localization
Authors: Kang Tang, Sheng Xu, Yuqi Yang, He Kong, Yongsheng Ma | Date: 2025-04-11
This paper focuses on static source localization employing different combinations of measurements, including time-difference-of-arrival (TDOA), received-signal-strength (RSS), angle-of-arrival (AOA), and time-of-arrival (TOA) measurements. Since sensor-source geometry significantly impacts localizat ... more
-5.535 Embedding Graphs as Euclidean kNN-Graphs
Authors: T. Schibler, S. Suri, J. Xue | Date: 2025-04-11
Let G = (V, E) be a directed graph on n vertices where each vertex has out-degree k. We say that G is kNN-realizable in d-dimensional Euclidean space if there exists a point set P = {p1, p2, ..., pn} in R^d along with a one-to-one mapping phi: V -> P such that for any u, v in V, u is an out-neighbor ... more
-5.539 Inverter Output Impedance Estimation in Power Networks: A Variable Direction Forgetting Recursive-Least-Square Algorithm Based Approach
Authors: Jaesang Park, Alireza Askarian, Srinivasa Salapaka | Date: 2025-04-11
As inverter-based loads and energy sources become increasingly prevalent, accurate estimation of line impedance between inverters and the grid is essential for optimizing performance and enhancing control strategies. This paper presents a non-invasive method for estimating output-line impedance usin ... more
-5.5408 Recursive PAC-Bayes: A Frequentist Approach to Sequential Prior Updates with No Information Loss
Authors: Yi-Shan Wu, Yijie Zhang, Badr-Eddine Ch\'erief-Abdellatif, Yevgeny Seldin | Date: 2025-04-11
PAC-Bayesian analysis is a frequentist framework for incorporating prior knowledge into learning. It was inspired by Bayesian learning, which allows sequential data processing and naturally turns posteriors from one processing step into priors for the next. However, despite two and a half decades of ... more
-5.5646 Analyzing How Text-to-Image Models Represent Nationalities in Everyday Tasks
Authors: Abdulkareem Alsudais | Date: 2025-04-11
The primary objective of this paper is to investigate how a popular Text-to-Image (T2I) model represents people from 208 different nationalities when prompted to generate images of individuals performing typical everyday tasks. Two scenarios were developed, and images were generated based on input p ... more
-5.5719 Optimal Duration of Reserve Capacity Ancillary Services for Distributed Energy Resources
Authors: Lorenzo Zapparoli, Blazhe Gjorgiev, Giovanni Sansavini | Date: 2025-04-11
The increasing integration of distributed energy resources (DERs) into power systems presents opportunities and challenges for ancillary services (AS) provision. Technical requirements of existing AS (i.e., duration, reliability, ramp rate, and lead time) have been designed for traditional generatin ... more
-5.572 Interactive Expressive Motion Generation Using Dynamic Movement Primitives
Authors: Till Hielscher, Andreas Bulling, Kai O. Arras | Date: 2025-04-11
Our goal is to enable social robots to interact autonomously with humans in a realistic, engaging, and expressive manner. The 12 Principles of Animation [1] are a well-established framework animators use to create movements that make characters appear convincing, dynamic, and emotionally expressive. ... more
-5.5769 We've Got You Covered: Type-Guided Repair of Incomplete Input Generators
Authors: Patrick LaFontaine, Zhe Zhou, Ashish Mishra, Suresh Jagannathan, Benjamin Delaware | Date: 2025-04-11
Property-based testing is a popular technique for automatically testing semantic properties of a program, specified as a pair of pre- and post-conditions. The efficacy of this approach depends on being able to quickly generate inputs that meet the precondition, in order to maximize the set of progra ... more
-5.5789 Quantum Combine and Conquer and Its Applications to Sublinear Quantum Convex Hull and Maxima Set Construction
Authors: Shion Fukuzawa, Michael T. Goodrich, Sandy Irani | Date: 2025-04-11
We introduce a quantum algorithm design paradigm called combine and conquer, which is a quantum version of the "marriage-before-conquest" technique of Kirkpatrick and Seidel. In a quantum combine-and-conquer algorithm, one performs the essential computation of the combine step of a quantum divide-an ... more
-5.5813 ICPS: Real-Time Resource Configuration for Cloud Serverless Functions Considering Affinity
Authors: Long Chen, Xinshuai Hua, Jinquan Zhang, Wenshuai Li, Xiaoping Li, Shijie Guo | Date: 2025-04-11
Serverless computing, with its operational simplicity and on-demand scalability, has become a preferred paradigm for deploying workflow applications. However, resource allocation for workflows, particularly those with branching structures, is complicated by cold starts and network delays between dep ... more
-5.591 LVC: A Lightweight Compression Framework for Enhancing VLMs in Long Video Understanding
Authors: Ziyi Wang, Haoran Wu, Yiming Rong, Deyang Jiang, Yixin Zhang, Yunlong Zhao, Shuang Xu, Bo XU | Date: 2025-04-11
Long video understanding is a complex task that requires both spatial detail and temporal awareness. While Vision-Language Models (VLMs) obtain frame-level understanding capabilities through multi-frame input, they suffer from information loss due to the sparse sampling strategy. In contrast, Video ... more
-5.5928 Domain Generalization through Attenuation of Domain-Specific Information
Authors: Reiji Saito, Kazuhiro Hotta | Date: 2025-04-11
In this paper, we propose a new evaluation metric called Domain Independence (DI) and Attenuation of Domain-Specific Information (ADSI) which is specifically designed for domain-generalized semantic segmentation in automotive images. DI measures the presence of domain-specific information: a lower D ... more
-5.6062 Adaptive Locally Linear Embedding
Authors: Ali Goli, Mahdieh Alizadeh, Hadi Sadoghi Yazdi | Date: 2025-04-11
Manifold learning techniques, such as Locally linear embedding (LLE), are designed to preserve the local neighborhood structures of high-dimensional data during dimensionality reduction. Traditional LLE employs Euclidean distance to define neighborhoods, which can struggle to capture the intrinsic g ... more
-5.6367 BIA Transmission in Rate Splitting-based Optical Wireless Networks
Authors: Ahmad Adnan Qidan, Khulood Alazwary, Taisir El-Gorashi, Majid Safari, Harald Haas, Richard V. Penty, Ian H. White, Jaafar M. H. Elmirghani | Date: 2025-04-11
Optical wireless communication (OWC) has recently received massive interest as a new technology that can support the enormous data traffic increasing on daily basis. In particular, laser-based OWC networks can provide terabits per second (Tbps) aggregate data rates. However, the emerging OWC network ... more
-5.6372 Machine Learning (ML) based Reduced Order Modeling (ROM) for linear and non-linear solid and structural mechanics
Authors: Mikhael Tannous (Arts et Metiers Institute of Technology, Paris, France), Chady Ghnatios (University of North Florida, Jacklsonville, United States), Eivind Fonn (SINTEF Digital, Trondheim, Norway), Trond Kvamsdal (Norwegian University of Science and Technology, Trondheim, Norway, SINTEF Digital, Trondheim, Norway), Francisco Chinesta (Arts et Metiers Institute of Technology, Paris, France) | Date: 2025-04-11
Multiple model reduction techniques have been proposed to tackle linear and non linear problems. Intrusive model order reduction techniques exhibit high accuracy levels, however, they are rarely used as a standalone industrial tool, because of the required high level knowledge involved in the constr ... more
-5.6408 Unifying monitoring and modelling of water concentration levels in surface waters
Authors: Peter B Sorensen, Anders Nielsen, Peter E Holm, Poul L Bjerg, Denitza Voutchkova, L{\ae}rke Thorling, Dorte Rasmussen, Hans Estrup, Christian F Damgaard | Date: 2025-04-11
Accurate prediction of expected concentrations is essential for effective catchment management, requiring both extensive monitoring and advanced modeling techniques. However, due to limitations in the equation solving capacity, the integration of monitoring and modeling has been suffering suboptimal ... more
-5.6427 UAV Position Estimation using a LiDAR-based 3D Object Detection Method
Authors: Uthman Olawoye, Jason N. Gross | Date: 2025-04-11
This paper explores the use of applying a deep learning approach for 3D object detection to compute the relative position of an Unmanned Aerial Vehicle (UAV) from an Unmanned Ground Vehicle (UGV) equipped with a LiDAR sensor in a GPS-denied environment. This was achieved by evaluating the LiDAR sens ... more
-5.6457 Computation of shape Taylor expansions
Authors: Gang Bao, Jun Lai, Haoran Ma | Date: 2025-04-11
Shape derivative is an important analytical tool for studying scattering problems involving perturbations in scatterers. Many applications, including inverse scattering, optimal design, and uncertainty quantification, are based on shape derivatives. However, computing high order shape derivatives is ... more
-5.6471 Restoring Feasibility in Power Grid Optimization: A Counterfactual ML Approach
Authors: Mostafa Mohammadian, Anna Van Boven, Kyri Baker | Date: 2025-04-11
Electric power grids are essential components of modern life, delivering reliable power to end-users while adhering to a multitude of engineering constraints and requirements. In grid operations, the Optimal Power Flow problem plays a key role in determining cost-effective generator dispatch that sa ... more
-5.6746 ER-RAG: Enhance RAG with ER-Based Unified Modeling of Heterogeneous Data Sources
Authors: Yikuan Xia, Jiazun Chen, Yirui Zhan, Suifeng Zhao, Weipeng Jiang, Chaorui Zhang, Wei Han, Bo Bai, Jun Gao | Date: 2025-04-11
Large language models (LLMs) excel in question-answering (QA) tasks, and retrieval-augmented generation (RAG) enhances their precision by incorporating external evidence from diverse sources like web pages, databases, and knowledge graphs. However, current RAG methods rely on agent-specific strategi ... more
LLMs RAG
-5.6751 International Scientific Report on the Safety of Advanced AI (Interim Report)
Authors: Yoshua Bengio, S\"oren Mindermann, Daniel Privitera, Tamay Besiroglu, Rishi Bommasani, Stephen Casper, Yejin Choi, Danielle Goldfarb, Hoda Heidari, Leila Khalatbari, Shayne Longpre, Vasilios Mavroudis, Mantas Mazeika, Kwan Yee Ng, Chinasa T. Okolo, Deborah Raji, Theodora Skeadas, Florian Tram\`er, Bayo Adekanmbi, Paul Christiano, David Dalrymple, Thomas G. Dietterich, Edward Felten, Pascale Fung, Pierre-Olivier Gourinchas, Nick Jennings, Andreas Krause, Percy Liang, Teresa Ludermir, Vidushi Marda, Helen Margetts, John A. McDermid, Arvind Narayanan, Alondra Nelson, Alice Oh, Gopal Ramchurn, Stuart Russell, Marietje Schaake, Dawn Song, Alvaro Soto, Lee Tiedrich, Ga\"el Varoquaux, Andrew Yao, Ya-Qin Zhang | Date: 2025-04-11
This is the interim publication of the first International Scientific Report on the Safety of Advanced AI. The report synthesises the scientific understanding of general-purpose AI -- AI that can perform a wide variety of tasks -- with a focus on understanding and managing its risks. A diverse group ... more
-5.6972 On the Compressibility of Integral Operators in Anisotropic Wavelet Coordinates
Authors: Helmut Harbrecht, Remo von Rickenbach | Date: 2025-04-11
The present article is concerned with the s*-compressibility of classical boundary integral operators in anisotropic wavelet coordinates. Having the s*-compressibility at hand, one can design adaptive wavelet algorithms which are asymptotically optimal, meaning that any target accuracy can be achiev ... more
-5.7179 Architecture independent generalization bounds for overparametrized deep ReLU networks
Authors: Thomas Chen, Chun-Kai Kevin Chien, Patricia Mu\~noz Ewald, Andrew G. Moore | Date: 2025-04-11
We prove that overparametrized neural networks are able to generalize with a test error that is independent of the level of overparametrization, and independent of the Vapnik-Chervonenkis (VC) dimension. We prove explicit bounds that only depend on the metric geometry of the test and training sets, ... more
-5.7195 Robust and Noise-resilient Long-Term Prediction of Spatiotemporal Data Using Variational Mode Graph Neural Networks with 3D Attention
Authors: Osama Ahmad, Zubair Khalid | Date: 2025-04-11
This paper focuses on improving the robustness of spatiotemporal long-term prediction using a variational mode graph convolutional network (VMGCN) by introducing 3D channel attention. The deep learning network for this task relies on historical data inputs, yet real-time data can be corrupted by sen ... more
-5.7694 InstantSticker: Realistic Decal Blending via Disentangled Object Reconstruction
Authors: Yi Zhang, Xiaoyang Huang, Yishun Dou, Yue Shi, Rui Shi, Ye Chen, Bingbing Ni, Wenjun Zhang | Date: 2025-04-11
We present InstantSticker, a disentangled reconstruction pipeline based on Image-Based Lighting (IBL), which focuses on highly realistic decal blending, simulates stickers attached to the reconstructed surface, and allows for instant editing and real-time rendering. To achieve stereoscopic impressio ... more
-5.7717 GBG++: A Fast and Stable Granular Ball Generation Method for Classification
Authors: Qin Xie, Qinghua Zhang, Shuyin Xia, Fan Zhao, Chengying Wu, Guoyin Wang, Weiping Ding | Date: 2025-04-11
Granular ball computing (GBC), as an efficient, robust, and scalable learning method, has become a popular research topic of granular computing. GBC includes two stages: granular ball generation (GBG) and multi-granularity learning based on the granular ball (GB). However, the stability and efficien ... more
-5.7829 A Survey of Source Code Representations for Machine Learning-Based Cybersecurity Tasks
Authors: Beatrice Casey, Joanna C. S. Santos, George Perry | Date: 2025-04-11
Machine learning techniques for cybersecurity-related software engineering tasks are becoming increasingly popular. The representation of source code is a key portion of the technique that can impact the way the model is able to learn the features of the source code. With an increasing number of the ... more
-5.8072 Pruner: A Draft-then-Verify Exploration Mechanism to Accelerate Tensor Program Tuning
Authors: Liang Qiao, Jun Shi, Xiaoyu Hao, Xi Fang, Sen Zhang, Minfan Zhao, Ziqi Zhu, Junshi Chen, Hong An, Xulong Tang, Bing Li, Honghui Yuan, Xinyang Wang | Date: 2025-04-11
Tensor program tuning is essential for the efficient deployment of deep neural networks. Search-based approaches have demonstrated scalability and effectiveness in automatically finding high-performance programs for specific hardware. However, the search process is often inefficient, taking hours or ... more
-5.808 Studying and Understanding the Effectiveness and Failures of Conversational LLM-Based Repair
Authors: Aolin Chen, Haojun Wu, Qi Xin, Steven P. Reiss, Jifeng Xuan | Date: 2025-04-11
Automated program repair (APR) is designed to automate the process of bug-fixing. In recent years, thanks to the rapid development of large language models (LLMs), automated repair has achieved remarkable progress. Advanced APR techniques powered by conversational LLMs, most notably ChatGPT, have ex ... more
-5.823 Untangling Lariats: Subgradient Following of Variationally Penalized Objectives
Authors: Kai-Chia Mo, Shai Shalev-Shwartz, Nis{\ae}l Sh\'artov | Date: 2025-04-11
We describe an apparatus for subgradient-following of the optimum of convex problems with variational penalties. In this setting, we receive a sequence $y_i,\ldots,y_n$ and seek a smooth sequence $x_1,\ldots,x_n$. The smooth sequence needs to attain the minimum Bregman divergence to an input sequenc ... more
-5.8509 A Crosstalk-Aware Timing Prediction Method in Routing
Authors: Leilei Jin, Jiajie Xu, Wenjie Fu, Hao Yan, Longxing Shi | Date: 2025-04-11
With shrinking interconnect spacing in advanced technology nodes, existing timing predictions become less precise due to the challenging quantification of crosstalk-induced delay. During the routing, the crosstalk effect is typically modeled by predicting coupling capacitance with congestion informa ... more
-5.8713 SPIRe: Boosting LLM Inference Throughput with Speculative Decoding
Authors: Sanjit Neelam, Daniel Heinlein, Vaclav Cvicek, Akshay Mishra, Reiner Pope | Date: 2025-04-11
Speculative decoding (SD) has been shown to reduce the latency of autoregressive decoding (AD) by 2-3x for small batch sizes. However, increasing throughput and therefore reducing the cost per token requires decoding with large batch sizes. Recent work shows that SD can accelerate decoding with larg ... more
LLMs
-5.8971 Temporal-contextual Event Learning for Pedestrian Crossing Intent Prediction
Authors: Hongbin Liang, Hezhe Qiao, Wei Huang, Qizhou Wang, Mingsheng Shang, Lin Chen | Date: 2025-04-11
Ensuring the safety of vulnerable road users through accurate prediction of pedestrian crossing intention (PCI) plays a crucial role in the context of autonomous and assisted driving. Analyzing the set of observation video frames in ego-view has been widely used in most PCI prediction methods to for ... more
-5.9398 Concurrent Composition for Differentially Private Continual Mechanisms
Authors: Monika Henzinger, Roodabeh Safavi, Salil Vadhan | Date: 2025-04-11
Many intended uses of differential privacy involve a $\textit{continual mechanism}$ that is set up to run continuously over a long period of time, making more statistical releases as either queries come in or the dataset is updated. In this paper, we give the first general treatment of privacy again ... more
-5.9792 Open Problems and a Hypothetical Path Forward in LLM Knowledge Paradigms
Authors: Xiaotian Ye, Mengqi Zhang, Shu Wu | Date: 2025-04-11
Knowledge is fundamental to the overall capabilities of Large Language Models (LLMs). The knowledge paradigm of a model, which dictates how it encodes and utilizes knowledge, significantly affects its performance. Despite the continuous development of LLMs under existing knowledge paradigms, issues ... more
-6.0037 Carbon-Efficient Software Design and Development: A Systematic Literature Review
Authors: Ornela Danushi, Stefano Forti, Jacopo Soldani | Date: 2025-04-11
The ICT sector, responsible for 2% of global carbon emissions, is under scrutiny calling for methodologies and tools to design and develop software in an environmentally sustainable-by-design manner. However, the software engineering solutions for designing and developing carbon-efficient software a ... more
-6.0919 Learning Generalizable Features for Tibial Plateau Fracture Segmentation Using Masked Autoencoder and Limited Annotations
Authors: Peiyan Yue, Die Cai, Chu Guo, Mengxing Liu, Jun Xia, Yi Wang | Date: 2025-04-11
Accurate automated segmentation of tibial plateau fractures (TPF) from computed tomography (CT) requires large amounts of annotated data to train deep learning models, but obtaining such annotations presents unique challenges. The process demands expert knowledge to identify diverse fracture pattern ... more
-6.1082 The Future of IPTV: Security, AI Integration, 5G, and Next-Gen Streaming
Authors: Georgios Giannakopoulos, Peter Adegbenro, Maria Antonnette Perez | Date: 2025-04-11
The evolution of Internet Protocol Television (IPTV) has transformed the landscape of digital broadcasting by leveraging high-speed internet connectivity to deliver high-quality multimedia content. IPTV provides a dynamic and interactive television experience through managed networks, ensuring super ... more
-6.2091 Beyond authorship: Analyzing contributions in PLOS ONE and the challenges of appropriate attribution
Authors: Abdelghani Maddi (GEMASS), Jaime Teixeira Da Silva (MIDAP) | Date: 2025-04-11
Purpose This study aims to evaluate the accuracy of authorship attributions in scientific publications, focusing on the fairness and precision of individual contributions within academic works. Design/methodology/approach The study analyzes 81,823 publications from the journal PLOS ONE , covering th ... more
-6.3536 A Novel Algorithm for Periodic Conformal Flattening of Genus-one and Multiply Connected Genus-zero Surfaces
Authors: Zhong-Heng Tan, Tiexiang Li, Wen-Wei Lin, Shing-Tung Yau | Date: 2025-04-11
In this paper, we propose a novel parameterization method for genus-one and multiply connected genus-zero surfaces, called periodic conformal flattening. The conformal energy minimization technique is utilized to compute the desired conformal map, which is characterised as an easily solvable quadrat ... more
-6.4365 Accelerated Stein Variational Gradient Flow
Authors: Viktor Stein, Wuchen Li | Date: 2025-04-11
Stein variational gradient descent (SVGD) is a kernel-based particle method for sampling from a target distribution, e.g., in generative modeling and Bayesian inference. SVGD does not require estimating the gradient of the log-density, which is called score estimation. In practice, SVGD can be slow ... more
-8.2145 GraspClutter6D: A Large-scale Real-world Dataset for Robust Perception and Grasping in Cluttered Scenes
Authors: Seunghyeok Back, Joosoon Lee, Kangmin Kim, Heeseon Rho, Geonhyup Lee, Raeyoung Kang, Sangbeom Lee, Sangjun Noh, Youngjin Lee, Taeyeop Lee, Kyoobin Lee | Date: 2025-04-11
Robust grasping in cluttered environments remains an open challenge in robotics. While benchmark datasets have significantly advanced deep learning methods, they mainly focus on simplistic scenes with light occlusion and insufficient diversity, limiting their applicability to practical scenarios. We ... more
Robotics
-8.3404 A Centralized Planning and Distributed Execution Method for Shape Filling with Homogeneous Mobile Robots
Authors: Shuqing Liu, Rong Su, Karl H. Johansson | Date: 2025-04-11
Nature has inspired humans in different ways. The formation behavior of animals can perform tasks that exceed individual capability. For example, army ants could transverse gaps by forming bridges, and fishes could group up to protect themselves from predators. The pattern formation task is essentia ... more
Robotics
-8.4417 Software Reconfiguration in Robotics
Authors: Sven Peldszus, Davide Brugali, Daniel Str\"uber, Patrizio Pelliccione, Thorsten Berger | Date: 2025-04-11
Robots often need to be reconfigurable$-$to customize, calibrate, or optimize robots operating in varying environments with different hardware). A particular challenge in robotics is the automated and dynamic reconfiguration to load and unload software components, as well as parameterizing them. Ove ... more
Robotics
-9.2997 GigaHands: A Massive Annotated Dataset of Bimanual Hand Activities
Authors: Rao Fu, Dingxi Zhang, Alex Jiang, Wanjia Fu, Austin Funk, Daniel Ritchie, Srinath Sridhar | Date: 2025-04-11
Understanding bimanual human hand activities is a critical problem in AI and robotics. We cannot build large models of bimanual activities because existing datasets lack the scale, coverage of diverse hand activities, and detailed annotations. We introduce GigaHands, a massive annotated dataset capt ... more
Robotics
-10.8725 The Schwurbelarchiv: a German Language Telegram dataset for the Study of Conspiracy Theories
Authors: Mathias Angermaier, Joao Pinheiro-Neto, Elisabeth Hoeldrich, Jana Lasser | Date: 2025-04-11
Sociality borne by language, as is the predominant digital trace on text-based social media platforms, harbours the raw material for exploring multiple social phenomena. Distinctively, the messaging service Telegram provides functionalities that allow for socially interactive as well as one-to-many ... more
Datasets
-11.7417 SCI-Reason: A Dataset with Chain-of-Thought Rationales for Complex Multimodal Reasoning in Academic Areas
Authors: Chenghao Ma, Haihong E., Junpeng Ding, Jun Zhang, Ziyan Ma, Huang Qing, Bofei Gao, Liang Chen, Meina Song | Date: 2025-04-11
Large Language Models (LLMs) and Large Multimodal Models (LMMs) demonstrate impressive problem-solving skills in many tasks and domains. However, their ability to reason with complex images in academic domains has not been systematically investigated. To bridge this gap, we present SCI-Reason, a dat ... more
Datasets
-15.0088 Evaluating Mutation Techniques in Genetic Algorithm-Based Quantum Circuit Synthesis
Authors: Michael K\"olle, Tom Bintener, Maximilian Zorn, Gerhard Stenzel, Leo S\"unkel, Thomas Gabor, Claudia Linnhoff-Popien | Date: 2025-04-11
Quantum computing leverages the unique properties of qubits and quantum parallelism to solve problems intractable for classical systems, offering unparalleled computational potential. However, the optimization of quantum circuits remains critical, especially for noisy intermediate-scale quantum (NIS ... more
Evolutionary Algorithms Quantum Computing
-16.7028 Hybrid CNN with Chebyshev Polynomial Expansion for Medical Image Analysis
Authors: Abhinav Roy, Bhavesh Gyanchandani, Aditya Oza | Date: 2025-04-11
Lung cancer remains one of the leading causes of cancer-related mortality worldwide, with early and accurate diagnosis playing a pivotal role in improving patient outcomes. Automated detection of pulmonary nodules in computed tomography (CT) scans is a challenging task due to variability in nodule s ... more
Medicine
-16.7657 Setup-Invariant Augmented Reality for Teaching by Demonstration with Surgical Robots
Authors: Alexandre Banks, Richard Cook, Septimiu E. Salcudean | Date: 2025-04-11
Augmented reality (AR) is an effective tool in robotic surgery education as it combines exploratory learning with three-dimensional guidance. However, existing AR systems require expert supervision and do not account for differences in the mentor and mentee robot configurations. To enable novices to ... more
Medicine
-16.7705 A Lightweight and Extensible Cell Segmentation and Classification Model for Whole Slide Images
Authors: Nikita Shvetsov, Thomas K. Kilvaer, Masoud Tafavvoghi, Anders Sildnes, Kajsa M{\o}llersen, Lill-Tove Rasmussen Busund, Lars Ailo Bongo | Date: 2025-04-11
Developing clinically useful cell-level analysis tools in digital pathology remains challenging due to limitations in dataset granularity, inconsistent annotations, high computational demands, and difficulties integrating new technologies into workflows. To address these issues, we propose a solutio ... more
Medicine
-16.8174 Leveraging Anatomical Priors for Automated Pancreas Segmentation on Abdominal CT
Authors: Anisa V. Prasad, Tejas Sudharshan Mathai, Pritam Mukherjee, Jianfei Liu, Ronald M. Summers | Date: 2025-04-11
An accurate segmentation of the pancreas on CT is crucial to identify pancreatic pathologies and extract imaging-based biomarkers. However, prior research on pancreas segmentation has primarily focused on modifying the segmentation model architecture or utilizing pre- and post-processing techniques. ... more
Medicine
-16.9254 Deep Learning for Cardiovascular Risk Assessment: Proxy Features from Carotid Sonography as Predictors of Arterial Damage
Authors: Christoph Balada, Aida Romano-Martinez, Vincent ten Cate, Katharina Geschke, Jonas Tesarz, Paul Cla{\ss}en, Alexander K. Schuster, Dativa Tibyampansha, Karl-Patrik Kresoja, Philipp S. Wild, Sheraz Ahmed, Andreas Dengel | Date: 2025-04-11
In this study, hypertension is utilized as an indicator of individual vascular damage. This damage can be identified through machine learning techniques, providing an early risk marker for potential major cardiovascular events and offering valuable insights into the overall arterial condition of ind ... more
Medicine
-17.107 Determining Fetal Orientations From Blind Sweep Ultrasound Video
Authors: Jakub Maciej Wi\'sniewski, Anders Nymark Christensen, Mary Le Ngo, Martin Gr{\o}nneb{\ae}k Tolsgaard, Chun Kit Wong | Date: 2025-04-11
Cognitive demands of fetal ultrasound examinations pose unique challenges among clinicians. With the goal of providing an assistive tool, we developed an automated pipeline for predicting fetal orientation from ultrasound videos acquired following a simple blind sweep protocol. Leveraging on a pre-t ... more
Medicine
-17.2021 EasyVis2: A Real Time Multi-view 3D Visualization System for Laparoscopic Surgery Training Enhanced by a Deep Neural Network YOLOv8-Pose
Authors: Yung-Hong Sun, Gefei Shen, Jiangang Chen, Jayer Fernandes, Amber L. Shada, Charles P. Heise, Hongrui Jiang, Yu Hen Hu | Date: 2025-04-11
EasyVis2 is a system designed to provide hands-free, real-time 3D visualization for laparoscopic surgery. It incorporates a surgical trocar equipped with an array of micro-cameras, which can be inserted into the body cavity to offer an enhanced field of view and a 3D perspective of the surgical proc ... more
Medicine
-18.0274 MedPix 2.0: A Comprehensive Multimodal Biomedical Data set for Advanced AI Applications with Retrieval Augmented Generation and Knowledge Graphs
Authors: Irene Siragusa, Salvatore Contino, Massimo La Ciura, Rosario Alicata, Roberto Pirrone | Date: 2025-04-11
The increasing interest in developing Artificial Intelligence applications in the medical domain, suffers from the lack of high-quality data set, mainly due to privacy-related issues. In addition, the recent increase in Vision Language Models (VLM) leads to the need for multimodal medical data sets, ... more
Medicine
-18.6013 Efficient Deployment of Spiking Neural Networks on SpiNNaker2 for DVS Gesture Recognition Using Neuromorphic Intermediate Representation
Authors: Sirine Arfa, Bernhard Vogginger, Chen Liu, Johannes Partzsch, Mark Schone, Christian Mayr | Date: 2025-04-11
Spiking Neural Networks (SNNs) are highly energy-efficient during inference, making them particularly suitable for deployment on neuromorphic hardware. Their ability to process event-driven inputs, such as data from dynamic vision sensors (DVS), further enhances their applicability to edge computing ... more
SpikingNN
-19.6622 PETNet -- Coincident Particle Event Detection using Spiking Neural Networks
Authors: Jan Debus, Charlotte Debus, G\"unther Dissertori, Markus G\"otz | Date: 2025-04-11
Spiking neural networks (SNN) hold the promise of being a more biologically plausible, low-energy alternative to conventional artificial neural networks. Their time-variant nature makes them particularly suitable for processing time-resolved, sparse binary data. In this paper, we investigate the pot ... more
SpikingNN
-19.8916 Assessing the risk of recurrence in early-stage breast cancer through H&E stained whole slide images
Authors: Geongyu Lee, Joonho Lee, Tae-Yeong Kwak, Sun Woo Kim, Youngmee Kwon, Chungyeul Kim, Hyeyoon Chang | Date: 2025-04-11
Accurate prediction of the likelihood of recurrence is important in the selection of postoperative treatment for patients with early-stage breast cancer. In this study, we investigated whether deep learning algorithms can predict patients' risk of recurrence by analyzing the pathology images of thei ... more
Medicine
-19.9324 Learning in Spiking Neural Networks with a Calcium-based Hebbian Rule for Spike-timing-dependent Plasticity
Authors: Willian Soares Gir\~ao, Nicoletta Risi, Elisabetta Chicca | Date: 2025-04-11
Understanding how biological neural networks are shaped via local plasticity mechanisms can lead to energy-efficient and self-adaptive information processing systems, which promises to mitigate some of the current roadblocks in edge computing systems. While biology makes use of spikes to seamless us ... more
SpikingNN
-22.4336 AstroClearNet: Deep image prior for multi-frame astronomical image restoration
Authors: Yashil Sukurdeep, Fausto Navarro, Tam\'as Budav\'ari | Date: 2025-04-11
Recovering high-fidelity images of the night sky from blurred observations is a fundamental problem in astronomy, where traditional methods typically fall short. In ground-based astronomy, combining multiple exposures to enhance signal-to-noise ratios is further complicated by variations in the poin ... more
Astronomy
-25.3865 Leveraging GCN-based Action Recognition for Teleoperation in Daily Activity Assistance
Authors: Thomas M. Kwok, Jiaan Li, Yue Hu | Date: 2025-04-11
Caregiving of older adults is an urgent global challenge, with many older adults preferring to age in place rather than enter residential care. However, providing adequate home-based assistance remains difficult, particularly in geographically vast regions. Teleoperated robots offer a promising solu ... more
GNN
-25.8769 Graph Neural Network-Based Distributed Optimal Control for Linear Networked Systems: An Online Distributed Training Approach
Authors: Zihao Song, Panos J. Antsaklis, Hai Lin | Date: 2025-04-11
In this paper, we consider the distributed optimal control problem for linear networked systems. In particular, we are interested in learning distributed optimal controllers using graph recurrent neural networks (GRNNs). Most of the existing approaches result in centralized optimal controllers with ... more
GNN
-26.262 GRAIN: Multi-Granular and Implicit Information Aggregation Graph Neural Network for Heterophilous Graphs
Authors: Songwei Zhao, Yuan Jiang, Zijing Zhang, Yang Yu, Hechang Chen | Date: 2025-04-11
Graph neural networks (GNNs) have shown significant success in learning graph representations. However, recent studies reveal that GNNs often fail to outperform simple MLPs on heterophilous graph tasks, where connected nodes may differ in features or labels, challenging the homophily assumption. Exi ... more
GNN
-27.4168 Benchmarking Convolutional Neural Network and Graph Neural Network based Surrogate Models on a Real-World Car External Aerodynamics Dataset
Authors: Sam Jacob Jacob, Markus Mrosek, Carsten Othmer, Harald K\"ostler | Date: 2025-04-11
Aerodynamic optimization is crucial for developing eco-friendly, aerodynamic, and stylish cars, which requires close collaboration between aerodynamicists and stylists, a collaboration impaired by the time-consuming nature of aerodynamic simulations. Surrogate models offer a viable solution to reduc ... more
GNN
-30.1952 SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation
Authors: Haoyu Liu, Ningyi Liao, Siqiang Luo | Date: 2025-04-11
Graph neural networks (GNNs) realize great success in graph learning but suffer from performance loss when meeting heterophily, i.e. neighboring nodes are dissimilar, due to their local and uniform aggregation. Existing attempts of heterophilous GNNs incorporate long-range or global aggregations to ... more
GNN
-36.2769 More-efficient Quantum Multivariate Mean Value Estimator from Generalized Grover Gate
Authors: Letian Tang | Date: 2025-04-11
In this work, we present an efficient algorithm for multivariate mean value estimation. Our algorithm outperforms previous work by polylog factors and nearly saturates the known lower bound. We find an algorithm that uses $O\left(n \log \frac{d}{\delta}\right)$ to achieve precision $\frac{\sqrt{\tex ... more
Quantum Computing
-37.7823 PHOENIX: Pauli-Based High-Level Optimization Engine for Instruction Execution on NISQ Devices
Authors: Zhaohui Yang, Dawei Ding, Chenghong Zhu, Jianxin Chen, Yuan Xie | Date: 2025-04-11
Variational quantum algorithms (VQA) based on Hamiltonian simulation represent a specialized class of quantum programs well-suited for near-term quantum computing applications due to its modest resource requirements in terms of qubits and circuit depth. Unlike the conventional single-qubit (1Q) and ... more
Quantum Computing
-38.8467 Quantum Reverse Shannon Theorem Revisited
Authors: Zahra Baghali Khanian, Debbie Leung | Date: 2025-04-11
Reverse Shannon theorems concern the use of noiseless channels to simulate noisy ones. This is dual to the usual noisy channel coding problem, where a noisy (classical or quantum) channel is used to simulate a noiseless one. The Quantum Reverse Shannon Theorem is extensively studied by Bennett and c ... more
Quantum Computing
-39.9124 GLT hidden structures in mean-field quantum spin systems
Authors: Christiaan J. F. van de Ven, Muhammad Faisal Khan, S. Serra-Capizzano | Date: 2025-04-11
This work explores structured matrix sequences arising in mean-field quantum spin systems. We express these sequences within the framework of generalized locally Toeplitz (GLT) $*$-algebras, leveraging the fact that each GLT matrix sequence has a unique GLT symbol. This symbol characterizes both the ... more
Quantum Computing
-40.3457 Context Switching for Secure Multi-programming of Near-Term Quantum Computers
Authors: Avinash Kumar, Meng Wang, Chenxu Liu, Ang Li, Prashant J. Nair, Poulami Das | Date: 2025-04-11
Multi-programming quantum computers improve device utilization and throughput. However, crosstalk from concurrent two-qubit CNOT gates poses security risks, compromising the fidelity and output of co-running victim programs. We design Zero Knowledge Tampering Attacks (ZKTAs), using which attackers c ... more
Quantum Computing
-40.5003 A Geometric-Aware Perspective and Beyond: Hybrid Quantum-Classical Machine Learning Methods
Authors: Azadeh Alavia, Hossein Akhoundib, Fatemeh Kouchmeshkib, Mojtaba Mahmoodianc, Sanduni Jayasinghec, Yongli Rena, Abdolrahman Alavi | Date: 2025-04-11
Geometric Machine Learning (GML) has shown that respecting non-Euclidean geometry in data spaces can significantly improve performance over naive Euclidean assumptions. In parallel, Quantum Machine Learning (QML) has emerged as a promising paradigm that leverages superposition, entanglement, and int ... more
Quantum Computing
-41.666 Computable and Faithful Lower Bound on Entanglement Cost
Authors: Xin Wang, Mingrui Jing, Chengkai Zhu | Date: 2025-04-11
Quantifying the minimum entanglement needed to prepare quantum states and implement quantum processes is a key challenge in quantum information theory. In this work, we develop computable and faithful lower bounds on the entanglement cost under quantum operations that completely preserve the positiv ... more
Quantum Computing
-45.4359 Continuous-Variable Quantum Encoding Techniques: A Comparative Study of Embedding Techniques and Their Impact on Machine Learning Performance
Authors: Minati Rath, Hema Date | Date: 2025-04-11
This study explores the intersection of continuous-variable quantum computing (CVQC) and classical machine learning, focusing on CVQC data encoding techniques, including Displacement encoding and squeezing encoding, alongside Instantaneous Quantum Polynomial (IQP) encoding from discrete quantum comp ... more
Quantum Computing
-53.4041 Security Vulnerabilities in Ethereum Smart Contracts: A Systematic Analysis
Authors: Jixuan Wu, Lei Xie, Xiaoqi Li | Date: 2025-04-11
Smart contracts are a secure and trustworthy application that plays a vital role in decentralized applications in various fields such as insurance,the internet, and gaming. However, in recent years, smart contract security breaches have occurred frequently, and due to their financial properties, the ... more
-71.9787 More Efficient Stealth Address Protocol
Authors: Marija Mikic, Mihajlo Srbakoski, Strahinja Praska | Date: 2025-04-11
The integration of privacy-preserving transactions into public blockchains such as Ethereum remains a major challenge. The Stealth Address Protocol (SAP) provides recipient anonymity by generating unlinkable stealth addresses. Existing SAPs, such as the Dual-Key Stealth Address Protocol and the Curv ... more
Blockchain